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The use of crisper-cas systems is currently applied to cells cultivated in vitro. As control of the 'off target' effects of Crispr improves and Crispr is used in vivo, why won't the immune system neutralize it?
Usually not. the Crispr/Cas proteins can be delivered to the cell as DNA/RNA and the proteins will only exist inside the cell in low numbers.
Even in systems that deliver the protein from the outside in vivo almost always the proteins would be encapsulated in a delivery system to ensure they and any necessary accompanying nucleic acids get into the cell. Getting Crispr/Cas into a cell is difficult to do and they don't just pop in by themselves. They also require guide RNA, which will not easily stay whole in the blood stream and tissues for long.
if you want to just inject a fluid into the blood with unprotected CRISPR/CAS proteins, an immune reaction might stop them. Not all proteins produce an immune reaction. Sometimes just 2-3 proteins of an entire bacterium produce an immune reaction (antibodies). But then again its unlikely the proteins would have edited any genomes in any host cells even so.
CRISPR-Cas9 in genome editing: Its function and medical applications
The targeted genome modification using RNA-guided nucleases is associated with several advantages such as a rapid, easy, and efficient method that not only provides the manipulation and alteration of genes and functional studies for researchers, but also increases their awareness of the molecular basis of the disease and development of new and targeted therapeutic approaches. Different techniques have been emerged so far as the molecular scissors mediating targeted genome editing including zinc finger nuclease, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9). CRISPR-Cas9 is a bacterial immune system against viruses in which the single-strand RNA-guided Cas9 nuclease is linked to the targeted complementary sequences to apply changes. The advances made in the transfer, modification, and emergence of specific solutions have led to the creation of different classes of CRISPR-Cas9. Since this robust tool is capable of direct correction of disease-causing mutations, its ability to treat genetic disorders has attracted the tremendous attention of researchers. Considering the reported cases of nonspecific targeting of Cas9 proteins, many studies focused on enhancing the Cas9 features. In this regard, significant advances have been made in choosing guide RNA, new enzymes and methods for identifying misplaced targeting. Here, we highlighted the history and various direct aspects of CRISPR-Cas9, such as precision in genomic targeting, system transfer and its control over correction events with its applications in future biological studies, and modern treatment of diseases.
Keywords: CRISPR-Cas9 genome editing new treatments specific targeting.
CRISPR-Cas components work together to enhance protection from viruses
Credit: CC0 Public Domain
Researchers from Skoltech and their colleagues from Russia and the U.S. have shown that the two components of the bacterial CRISPR-Cas immunity system, one that destroys foreign genetic elements such as viruses and another that creates "memories" of foreign genetic elements by storing fragments of their DNA in a special location of bacterial genome, are physically linked. This link helps bacteria to efficiently update their immune memory when infected by mutant viruses that learned to evade the CRISPR-Cas defense. The paper was published in the journal Proceedings of the National Academy of Sciences.
CRISPR-Cas, a defense mechanism that provides bacteria with resistance to their viruses (bacteriophages), destroys DNA from previously encountered adversaries by 'comparing' it to spacers, short bits of genetic information stored as a 'library' in a "memory chip" located in a special site of the bacterial genome. CRISPR-Cas "learns" to recognize new enemies by incorporating new viral-derived spacers into this library during the infection. The two stages of CRISPR functioning, acquisition of spacers and their use to fight reinfections, are called adaptation and interference, correspondingly.
"To avoid CRISPR, phages acquire mutations that introduce mismatches with spacers. So in order to maintain an effective defense, the CRISPR-Cas system needs to update the set of spacers faster than mutant phages with escape mutations arise. To meet this requirement, CRISPR-Cas systems have evolved a special mechanism of primed adaptation. During primed adaptation, preexisting spacers that recognize a target, even inefficiently, promote very efficient acquisition of additional spacers from the same DNA molecule on which the target is located," Olga Musharova, Skoltech research scientist and the paper's first author, explains.
The exact molecular mechanism of priming is still unclear, but it requires tight coordination between the killing and the memorizing parts of the CRISPR mechanism. In the new paper, Skoltech Professor Konstantin Severinov, Musharova and their colleagues were able to confirm the existence of a priming complex that includes both the Cas1-Cas2 proteins responsible for acquiring new spacers, and the Cas3 protein that cleaves enemy DNA.
"The part that destroys foreign DNA and the part that acquires new information for the future protective function of CRISPR-Cas system are linked. It's as if the mallet in a Whac-A-Mole game could also take pictures of the moles for future reference," Severinov said.
In experiments with E. coli, the team showed that fragments of DNA being degraded by Cas3 are directly passed over to Cas1-Cas2 as "prespacers" that eventually become spacers. "This result is of fundamental significance. We uncovered the link between the interference and the adaptation processes," Musharova says. "Our findings also show how the CRISPR adaptation can be made more efficient, which is important for using bacterial populations for information storage."
The team plans to keep investigating primed adaptation in bacterial cells and finding the most efficient way to install desired "memories" in bacterial DNA in the form of spacers.
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To DSB or not to DSB
Relying on HDR for editing risks introducing indels or chromosomal translocations. Even with a precisely targeted nuclease, with HDR, “you’re at the mercy of the cell,” Stoddard observes. For editing without the unpredictability of HDR, he adds, watch for developments in site-specific recombinases (SSRs).
“SSRs do the whole thing,” says Marshall Stark, molecular geneticist at the University of Glasgow in Scotland. “They break and rejoin the DNA with no need for host factors.” Even in cells with low or no HDR, SSRs can integrate exogenous DNA at a targeted site. Under optimal conditions, Stark says, “SSRs can be extremely efficient, with recombination approaching 100% in a few minutes.” SSRs can make switch-like changes such as inverting a DNA segment’s orientation. This makes them valuable for creating electronic circuit–like pathways that control cell behavior for industrial purposes or synthetic biology applications, such as making biocomputers. However, SSRs have complex, rare, 30- to 50-basepair target sites.
Stark names two approaches to adapting SSRs for more widespread genome editing: (1) using directed evolution that selects for new target specificities and (2) making fusion proteins. For example, he and others are attaching SSR-derived recombinase domains to zinc-finger modules that bind specific DNA sequences. The technology is “still at the investigative level,” Stark notes. “If you have a particular target in mind, it’s still a lot of work to make a recombinase for it.”
For genome-editing without DSBs, researchers use Cas9 that is still directed by gRNAs but does not cut DNA or makes only single-stranded nicks. Cas9 variants are fused to transcription activators or repressors, or to enzymes that alter chromatin structure by modifying DNA or DNA-packaging histone proteins to change gene expression. This epigenome editing resembles natural gene regulation, Gersbach says, “without risk of off-target changes to DNA sequences.” The method is a basic research tool for studying epigenetics and has potential therapeutic uses, such as reactivating the silenced gene that causes the intellectual disability Fragile X syndrome (4).
Base editing makes single-basepair changes while avoiding unintended mutations from DSB repair. It works in cells without HDR. Innovations in this method are published regularly, but the first base editors developed by David Liu’s group at Harvard University had a disabled Cas9 targeting a DNA sequence fused to an enzyme that converts cytosine to uracil. The fusion protein changes a cytosine–guanine pair to thymine–adenine. Another base editor, which Liu’s lab generated through protein engineering and directed evolution, changes adenine–thymine to guanine–cytosine. Just these two base-editing systems can make one-third of all possible basepair changes, Liu asserts, and potentially correct 62% of known human pathogenic point mutations.
As of summer 2019, more than 100 research papers described experiments using base editing, Liu says, “including several that corrected animal models of human genetic diseases by directly reversing point mutations.” For example, one editor corrected a mutation that causes phenylketonuria (5). Liu and others are diversifying base editing—expanding base-changing options, increasing specificity, and improving activity in live animals and at target sites that require distinguishing between highly similar sequences.
CRISPR Clinical Trials: A 2021 Update
2020 was a big year for CRISPR &mdash the discoveries of new Cas proteins, use of CRISPR technology to study and develop diagnostic tests of COVID-19, a Nobel Prize, and more. The past year has also brought results from clinical trials using CRISPR technology, which we first reported on in 2019, and the start of new clinical trials.
In this new review article, we will go over the basics of clinical trials and then map out the current CRISPR-based trials from disease background to what we really hope to learn from these trials.
CLINICAL TRIAL BASICS
In the United States, the Food and Drug Administration (FDA) evaluates new disease treatments for safety and efficacy through clinical trials on patient volunteers. Early trials look at safety and side effects. Later trials test efficacy and compare new therapies with standard treatments.
The current trials using CRISPR-based treatments are still in early stages. That means that even if the treatments are safe and effective, they&rsquore likely still a few years away from FDA approval and being broadly available to patients.
The advent of CRISPR technology opens up new possibilities in precision medicine. Current trials are underway in five treatment areas: blood disorders, cancers, eye disease, chronic infections, and protein-folding disorders. All current CRISPR clinical trials are intended to edit specific cells or tissues without affecting sperm or eggs, meaning no DNA changes can be passed onto future generations.
Red blood cells use hemoglobin to carry oxygen from the lungs to all the tissues of the body. Mutation in a gene that encodes part of the hemoglobin molecule cause two different genetic disorders: sickle cell disease (SCD) and beta thalassemia.
In sickle cell disease (SCD), red blood cells are misshapen. Their crescent or &ldquosickle&rdquo shape makes them block blood vessels, slowing or stopping blood flow. This causes sudden, severe pain. Complications include chronic pain, organ damage, strokes, and anemia. In beta thalassemia, patients do not make enough hemoglobin. This leads to anemia and fatigue. In more severe cases, patients have organ damage, especially to the liver, bones, and heart. Both diseases can be fatal.
There are some treatments available, but often, patients still suffer severe symptoms and complications from their diseases. Patients with more severe SCD and beta thalassemia need frequent blood transfusions. Bone marrow transplant can be curative however, this can only be done when a healthy, matching donor can be found. This is not an option for most SCD or beta thalassemia patients.
The approach taken to treat blood disorders with CRISPR technology doesn&rsquot directly fix the gene variants that cause disease, but uses a clever workaround: instead of directly fixing the disease-causing mutations, the goal is to increase levels of fetal hemoglobin. This is a form of hemoglobin that fetuses make in the womb, but children and adults don&rsquot make. It is not entirely understood why humans switch from one form of hemoglobin to the other, but fetal hemoglobin can take the place of defective adult hemoglobin in red blood cells. This treatment can be used to treat both beta thalassemia and SCD.
In SCD patients, symptoms start to show after fetal hemoglobin (HbF) levels decrease.
The first step of treatment is to harvest a patient&rsquos blood stem cell from their blood. Next, scientists edit the genome of these cells. Then, chemotherapy eliminates the defective blood stem cells in the patient&rsquos body, and billions of genome-edited stem cells are put back into their bloodstream. The gene-edited blood stem cells are delivered by IV. If this approach works as intended, these cells will settle in and create a new blood stem cell population in the bone marrow, which will make edited red blood cells that produce fetal hemoglobin.
This treatment approach is considered ex vivo genome editing, because the editing occurs outside of the patient&rsquos body. Ex vivo editing guarantees that genome-editing tools only come in contact with the right target cells.
CURRENT CRISPR CLINICAL TRIALS
In the first use of an ex vivo CRISPR-based therapy to treat a genetic disease, researchers treated a patient with beta thalassemia in Germany in February 2019. 12 more patients have since been treated, and seven of them have been followed for at least three months. None of the patients needed blood transfusions in the months after treatment. The first patient with SCD was treated with the same therapy in Nashville, Tennessee in July 2019. This patient, Victoria Gray, has shown remarkable progress. Hear from Gray herself. Early results on other patients are promising, too:
- All patients treated for SCD or beta thalassemia are showing normal to near-normal hemoglobin levels, where at least 30% (SCD) or 40% (beta thalassemia) of hemoglobin is fetal hemoglobin.
- In bone marrow samples taken from Gray, an additional SCD patient, and five beta thalassemia patients, researchers found cells with the expected genetic edit that allows them to make fetal hemoglobin. This indicates that the edited cells have successfully taken up residence in the bone marrow.
- The only immediate side effects associated with the treatment resulted from the administration of chemotherapy.
- Read more:
- &mdash Frangoul et al., New England Journal of Medicine &mdash Abstract from Frangoul et al. at the American Society of Hematology
- The treatment was safe to administer and had acceptable side effects like fever, rash, and fatigue.
- The desired edit was found in a median of 6% of T cells/patient before infusion back into the patient.
- Off-target effects &mdash unwanted changes at various places in the genome &mdash were observed at a low frequency and were mostly in parts of the genome that don&rsquot code for proteins. On-target effects &mdash unwanted changes at the target site &mdash were more common (median of 1.69%).
- Edited T cells were found in 11 out of 12 patients two months after the infusion, although at low levels. Patients with higher levels of edited cells had less disease progression.
- Read more:
- &mdash Yu et al., Nature Medicine &mdash Lacey & Fraietta, Trends in Molecular Medicine
- The treatment was safe to administer and had acceptable side effects.
- The T cells took up residency in the bone marrow and remained at stable levels for the nine months of the study.
- Tumor biopsies showed that T cells were able to find and infiltrate tumors.
- Off-target effects were rarely observed. But, unwanted changes at the target site were observed frequently, with 70% of cells showing at least one mutation at or near the target site during manufacturing. After infusion and over time in patients, the percentage of cells with mutations decreased.
- Read more:
- &mdash Stadtmauer et al., Science
CRISPR Therapeutics and Vertex Pharmaceuticals are jointly running the fetal hemoglobin trials, and recruiting patients in the US, Canada, and Europe.
A forthcoming trial from UCSF, UCLA, and UC Berkeley, including IGI researchers, is planning to test an alternate approach that would directly repair the mutation that causes SCD.
WHAT TO WATCH FOR
Right now, there is a burst of research into treating blood disorders. Pharmaceutical and biotech companies as well as academic research institutions are working on both pharmaceutical and genetic therapies. We don&rsquot know yet which approaches will be the safest and most effective, but patients are sure to benefit from the increased research activity in these disease areas.
The initial results from Victoria Gray and other patient-volunteers are extremely encouraging. We look forward to more detailed results from other trial participants, who will hopefully show similar symptom reduction. Long-term follow-up of Gray and other patients is crucial: they will be tracked for years to come to see if the treatment remains effective, and to look for potential side effects, like negative health effects or cancer from unwanted or off-target edits, which won&rsquot be apparent until further down the line.
&ldquoThe main side effects so far have been from the chemotherapy necessary to wipe out the pre-existing bone marrow cells in order for the edited cells to engraft. The chemotherapy is a huge limiting factor for these therapies,&rdquo explains Megan Hochstrasser, Ph.D., a CRISPR expert who studied with Jennifer Doudna and now serves as the IGI Education Program Manager. &ldquoIf you have to be in the hospital for weeks because you are getting your bone marrow ablated with chemotherapy, which cripples your immune system, it&rsquos risky, expensive, and time-consuming. That&rsquos a huge barrier to scaling this and making it available widely. It&rsquos a big hurdle that could be overcome if someone finds a way to deliver the treatment directly, without bone marrow ablation.&rdquo
Scalability &mdash getting the treatment to the many people who need it &mdash will be a major challenge if the treatment move forward to FDA approval, both because of the technical challenges of creating the individualized product and administering the treatment protocol, and the cost. Research into in vivo approaches, which could eliminate the need for chemotherapy and decrease the associated risks and expenses, is in early stages, but will be a focus of those working to make more widely accessible CRISPR-based therapies for blood disorders in the coming years.
Cancer refers to a group of diseases that are caused by uncontrolled cell growth. Right now, CRISPR-based therapies are mainly aimed at treating blood cancers like leukemia and lymphoma. A trial in China for a type of lung cancer was recently completed, as well.
T cells, a type of white blood cell essential for immune system response, are covered in receptors that recognize other cells as safe or threatening. They patrol the body, killing foreign or dangerous cells, or recruiting other cells to assist. In CAR-T immunotherapy, researchers genetically engineer a patient&rsquos T cells to have a receptor that recognizes the patient&rsquos cancer cells, telling the T cells to attack. The immune system is highly regulated to avoid attacking healthy cells. Some T cell receptors work as &ldquocheckpoints&rdquo that determine whether an immune response occurs. When a T cell PD-1 receptor comes in contact with a molecule called PD-L1 on another cell, it communicates that it is a &ldquosafe&rdquo cell and the T cell leaves it alone.
Cancer cells are often cloaked in these molecular safety signals, tricking the patrolling T cells into ignoring them. Researchers are using CRISPR to edit the PD-1 gene in T cells to stop them from making functional PD-1 receptors so they can&rsquot be fooled by cancer cells. This immunotherapy approach is known as checkpoint inhibition, and it is often used in conjunction with CAR-T engineering to give T cells the greatest possible chance of eliminating cancer.
For these treatments, researchers harvest T cells from a patient&rsquos blood and engineer them in a lab. Then, they put them back into the patient&rsquos bloodstream by IV. Because this treatment relies on ex vivo editing, it is easy to deliver the genome-editing tools to the target cells. CAR-T therapy was approved for use in treating blood cancers in 2017.
CURRENT CRISPR CLINICAL TRIALS
In 2016, a lung cancer patient became the first person in the world to be treated with a CRISPR therapy: this patient was injected with PD-1 edited T cells in a Chinese clinical trial. This and an American clinical trial using CRISPR-based immunotherapies for cancer have been completed. Several other clinical trials using CRISPR-based immunotherapies, mainly to treat blood cancers, are ongoing.
In the Chinese study, researchers at the West China Hospital, Sichuan University treated 12 patients with non-small-cell lung cancer with PD-1 edited T cells. This approach did not include CAR-T, as it is not currently an option for lung cancers. The main goals of this study were to test whether the treatment was safe, had tolerable side effects, and whether it elicited a dangerous immune response.
In April 2020, results from this trial were published in Nature Medicine. The reported findings include:
The first CRISPR-based therapy trial in the US combined CAR-T and PD-1 immunotherapy approaches, using CRISPR to edit a total of three genes. This phase 1 study, run by the University of Pennsylvania in conjunction with the Parker Institute, began recruiting in 2018 and was completed in February 2020. Like the Chinese trial, the goals were to determine if the treatment was safe and had acceptable side-effects, not to cure patients. Two patient volunteers with advanced white blood cell cancer (myeloma) and one with metastatic bone cancer (sarcoma) were treated. The reported findings are:
Together, these studies indicate that CRISPR-engineered CAR-T therapy may be a promising line of treatment: it is safe, the side effects are tolerable, and the treatment does not induce a strong immune reaction.
Neither treatment provided a cure, but the aim of these studies was only to see if the treatment was safe and should be developed further for treatment, not to cure disease. As first attempts, both trials met their goals of showing safety and tolerability.
&ldquoA really interesting thing is that the American study did show a percentage of the large genomic rearrangements that people fear,&rdquo says Hochstrasser. &ldquoBut the percentage of cells with these changes actually decreased over time. It seemed like the cells that had those types of mutations were dying or getting out-competed by the other cells. So, it seemed like the cells that you wouldn&rsquot want in the body were not actually sticking around in the body, which was a surprise to me, and very encouraging.&rdquo
WHAT TO WATCH FOR
The FDA has already approved CAR-T therapies and PD-1 pathway inhibitors that don&rsquot use genome editing. This is a reason for optimism: the proof-of-principle work for these therapies has already been done successfully.
According to Hochstrasser, &ldquoThe efficiency of editing &mdash meaning, the percentage of cells that actually got edits &mdash wasn&rsquot great in either trial. But these trials were approved years ago, and done using technology from 2016. There have been many advancements in the technology since then. So, it&rsquos an important proof of concept around the immediate safety and tolerability of the treatment, and we&rsquoll have to see if they can increase editing efficiency.&rdquo
Using now-available, more potent techniques, will editing efficiency be higher? And with higher editing efficiency, will genetic checkpoint inhibition work as well or better than checkpoint-blocking drugs? Will PD-1 editing be as or more effective than antibody treatments that disable PD-1? Future research will have to answer these questions.
For the full review of CRISPR clinical trials underway in 2021, covering eye disease, chronic infection, a rare protein-folding disease, and what to look for next from CRISPR-based therapeutics, please see the complete article from the Innovative Genomics Institute.
By Hope Henderson, Innovative Genomics Institute, March 2021
CRISPR as a Probabilistic Memory of Phage.
We consider a model of infection where bacteria encounter j = 1 , … , K types of phage, each with probability f j . For simplicity, all types of phage are taken to be equally infectious and to have similar growth rates, conditions that are easily relaxed. In the CRISPR mechanism for adaptive immunity, bacteria incorporate snippets of phage DNA (spacers) into a CRISPR cassette. Upon later infection, the bacteria recruit CRISPR-Cas complexes with spacers that match the invading phage to cleave the viral DNA (Fig. 1).
CRISPR immunity in bacteria. A bacterium (bordered rectangle) with CRISPR machinery encounters a diverse set of phages (colors). The CRISPR-Cas locus is transcribed and then processed to bind Cas proteins (gray ovals) with distinct spacers (colors), thus producing CRISPR-Cas complexes. The complex with a spacer that is specific to the injected phage DNA (same color) can degrade the viral material and protect the bacterium from infection.
Suppose that the CRISPR cassettes contains L spacers in total and that an individual bacterium maintains a population of N p Cas protein complexes that can be recruited to cleave invaders. The spacer configuration can be characterized in terms of a vector s = < s 1 ⋯ s K >with entries counting the number of spacers specific to each phage type. The total cassette size is the sum of s j , i.e., ∑ j s j = L , and quantifies the amount of immune memory stored by an individual bacterium. We describe the phage configuration in a given infection event as a vector v of length K with entries indicating presence (1) or absence (0) of each viral type. Finally, we define the configuration of complexes d = < d 1 ⋯ d K >as a vector with entries counting the number complexes specific to each phage during the CRISPR response. The total number of complexes is the sum of d j , i.e., ∑ j d j = N p . In terms of these variables, the probability of surviving a phage infection using the CRISPR-Cas defense mechanism is P survival = 1 − ∑ v p V ( v ) ∑ s 1 + s 2 + ⋯ = L p S ( s ∣ L ) × ∑ d 1 + d 2 + ⋯ = N p 1 − α ( v , d ) q ( d ∣ s ) .  Here p V ( v ) is the probability of encountering the phage configuration v, p S ( s ∣ L ) is the probability of having a cassette configuration s of length L, α ( v , d ) is the probability of detecting all of the viral types present in v given the configuration d of complexes, and q ( d ∣ s ) is the probability of producing the CRISPR-Cas configuration d given the set of spacers s and N p Cas protein complexes.
Even if the number of phage types exceeds the number of complexes ( K ≫ N p ), bacteria can survive because we assume that a typical infection only involves a few viral types (or even just one). In this scenario, an infecting phage will attack a part of a large bacterial population. With cassettes sampling spacers randomly, at least some attacked individuals are likely to contain spacers specific to the phage and will thus survive. Innate mechanisms will also lead to survival of some bacteria without specific spacers, although we do not explicitly model this contribution to immunity. The surviving bacteria, and individuals which were not attacked, will replicate and maintain the population. In this context, when the next infection arrives, the cassettes in the bacterial population will again be effectively randomly drawn relative to the new infection. That is, although the cassettes will be enriched to reflect the previous infection, most of the spacers will still be randomly distributed so that cassettes in different individual bacteria will be largely uncorrelated. Over repeated encounters, the cycles of enrichment will lead to cassettes reflecting the distribution of phages. This scenario would be challenged if phages are very diverse ( K > > N p ), new infections occur rapidly (faster than bacterial replication times of 1/2 to 1 h), and infections carry large viral loads (relative to the colony size). In this case the bacterial population will not have time to equilibrate between attacks and may need other defense mechanisms besides CRISPR to survive.
The form of the detection probability function α ( v , d ) depends on the specific mechanism used by the CRISPR machinery to bind and degrade a phage. However, a critical number of specific complexes, d c , is required for the CRISPR machinery to achieve targeting at the speeds measured in experiments (32). This critical number depends implicitly on cell size, diffusion constants, timescales of phage infection and target recognition, and dissociation/association constants between spacers and protospacer sequences. Below this critical value, detection is less likely, and above the critical value the detection probability increases. We consider two functional forms for the probability that a particular phage type can be detected with d specific complexes: 1) a hard constraint α ( d ) = θ ( d − d c ) , where θ ( x ) is step function and is 0 if x < 0 and 1 if x > 0 , and 2) a soft constraint α ( d ) = d h d h + d c h . In both cases, d c is an efficacy parameter that depends on biochemical rates and determines the threshold on d below which detection is rare and above which detection is common. The first functional form (step function) describes switch-like behavior where complexes bind to phage DNA if they exceed a certain concentration d > d c . The second form mimics a Hill-like response, where the chance of binding increases gradually with the number of complexes. Here we allow the binding of Cas complexes with phage DNA binding to be cooperative. As the cooperativity h increases, the binding behavior becomes increasingly switch-like.
In our framework, we are assuming that spacer incorporation happens when the infecting phage is defective or if some other mechanism of immunity comes into play. We are then considering the subsequent probability of surviving phages due to CRISPR-Cas machinery when the spacer is already present. The probability of survival will increase when other forms of immunity (e.g., innate immunity and quorum-sensing effects) are also included. Importantly, CRISPR is not the first line of defense, and other antiphage mechanisms can precede or complement the CRISPR system (33). The net effect of these additional mechanisms can be modeled by including a nonzero baseline in the detection probability function α. Thus, our model describes the further survival advantage conferred by having CRISPR as a long-term memory of the phage landscape. Because of this, a bacterial population need not be in danger of extinction even if the CRISPR contribution to survival is relatively small.
There Is an Optimal Amount of Memory.
In realistic settings we can make simplifying assumptions about the general model in Eq. 1. For example, we can assume that successful infections of a bacterium by different phages occur with low probability and are independent. Of course, a given bacterium can be infected by multiple phages over its lifetime. Since the probability that a bacterium simultaneously encounters multiple phages is small, we assume that encounters are sequential (i.e., the viral configuration vector v has a single nonzero entry).
Second, we assume that a bacterium’s lineage encounters many and diverse phage types over multiple generations, i.e., K ≫ 1 . Because phages mutate readily, there is subtlety about what defines a type. We use a functional definition—a type of phage is defined by its specific recognition by a given spacer. Sometimes, after a bacterium becomes immune to a phage, single point mutations in the virus can produces escapers that evade recognition. By our definition these escapers are effectively a new type of virus that the bacterial population must deal with sequentially in future infections (34 ⇓ ⇓ –37).
Third, we assume that spacers are uniformly sampled from the phage distribution over time. In other words, we are assuming that prokaryotes pick up spacers from phages as they are encountered. So, if they encounter diverse viruses, they will have diverse arrays, and the phage distribution will be naturally reflected in the distribution of spacers. Incorporating such a distribution is straightforward, by assuming that the probability p i that a spacer is incorporated is a function of the viral type i. However, this distribution is not known experimentally. Therefore, we make a minimal assumption that all phage types are equally likely and occur with probability 1 / K . This is a conservative assumption because bacterial immune memory confers the least advantage when faced with an unbiased (i.e., a minimally informative) phage environment. In effect, we focus on the long-term statistical features of immunity and not the the short-time coevolutionary arms race between bacterium and phage.
Finally, we assume that phage encounters from which spacers are acquired occur randomly. Thus, each spacer in the CRISPR cassette has a probability 1 / K of being specific for a given phage. Since the cassette size is much smaller than the number of viral types ( L ≪ K ), it also follows that the cassette will typically have one or no spacers that can target a particular phage type—in other words, s i = 0,1 (but see below for an analysis allowing multiple spacers from each phage).
In general, it is likely that the distribution of spacers is more uniform than the distribution of phages. Mechanistically, once a prokaryote has a spacer that works well for a given phage by targeting an evolutionarily conserved region in its genome, there will be less occasion in the long term to acquire many additional spacers from the same virus since the existing defenses work, although in the short term, CRISPR targeting may produce defective phages that encourage incorporation of additional spacers. (See SI Appendix for discussion of priming and the effects of multiple specific spacers.) On longer timescales, spacers from novel viruses are likely to be incorporated, leading to a distribution of spacers that is more uniform than the distribution of viruses. From a strategic standpoint, once there is a sufficiently effective defense against common threats, it is more statistically effective to devote the remaining resources preferentially to rare threats. Indeed, although phenomena like priming can lead to acquisition of multiple spacers against a given virus (23 ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ –30), several studies have shown that having just a few spacers from a given phage type is largely sufficient to neutralize reinfections (6, 8, 16 ⇓ ⇓ –19). This means that the distribution of spacers should be more uniform than the distribution of pathogens—i.e., more weight should be given to rare infections than warranted by their frequency, again suggesting that a uniform distribution of spacers will be a reasonable approximation. Similar observations were made concerning the vertebrate adaptive immune system in refs. 38 and 39. RNA-seq screening in laboratory conditions has shown variable expression of spacers in CRISPR cassettes, typically accompanied by a gradual decline from the leader end, although there can be internal promoters leading to enhanced expression of distal spacers (12 ⇓ –14). We will approximate these sporadic expression patterns as a constant average across spacers whose expression is high enough to mount a defense against phages.
We derive an expression for the probability to survive a phage infection, given the cassette size L, the number of Cas complexes N p , and the diversity of the phage population K (Materials and Methods and Eq. 3). Across a wide range of parameters and for various choices of detection probability functions α ( v , d ) , we find that there is an optimal amount of memory, consisting of a few tens of spacers in the CRISPR cassette, to maximize survival probability (Fig. 2 and Fig. S1). The optimum occurs because there is a tradeoff between the amount of stored memory in CRISPR cassettes and the efficacy with which a bacterium can utilize its limited resources (i.e., Cas proteins) to turn memory into a functional response. If the CRISPR cassette is too small, bacteria do not remember past phage encounters well enough to defend against future infections. On the other hand, if the cassette is too large, Cas complexes bind too infrequently to the correct spacer to provide effective immunity against a particular invading virus. The optimal amount of immune memory (cassette size) should lie in between these two extremes, with details that depend on the phage diversity, the number of Cas complexes, and the detection probability function α ( v , d ) (Fig. 2 and Materials and Methods).
There is an optimal amount of immune memory. The heat map shows the probability of surviving a phage infection, P survival , as a function of the cassette size and phage diversity with N p = 700 Cas complexes. P survival can be interpreted as the fractional population size that will persist after sequential phage attacks if CRISPR is the only defense mechanism. The detection probability function is a step function α ( d ) = θ ( d − d c ) with threshold d c = 8 , implying that detection of a phage requires at least d c complexes bound to the corresponding spacers. For any number of phage types, there is an optimal cassette size. See Fig. S1 for different choices of N p , d c , and functional forms for α.
Optimal Memory Depends on Phage Diversity.
How does the optimal amount of CRISPR memory depend on the diversity of viral threats? If there are relatively few types of phage, an optimal strategy for a bacterium would be to maintain an effective memory for most threats and to match the viral variants with a cassette whose size grows with viral diversity. This matching strategy will eventually fail as the diversity of the phage population increases if the number of Cas proteins ( N p ) is limited. We examined this tradeoff by measuring the optimal cassette size as we varied the viral diversity (K) while keeping the CRISPR machinery ( N p and the detection probability α ( v , d ) ) fixed.
To characterize viral diversity, we defined a parameter κ = K / N p as the ratio of the number of phage types (K) and the number of Cas complexes ( N p ). When phage diversity is low ( κ ≤ 1 ), the optimal amount of memory (number of spacers in a cell) increases sublinearly with viral heterogeneity (Fig. 3), approximately as a power law. When the detection probability α ( v , d ) is nearly switch-like, the optimal cassette size scales approximately as L ∼ K (Fig. 3 A and B analytic derivation for d c = 1 in SI Appendix). This implies that when viral diversity is low, the amount of memory should increase with the diversity, but it is actually beneficial not to retain a memory of all prior phage encounters. Forgetting some encounters will allow the bacterium to mount a stronger response against future threats by engaging a larger number of Cas complexes for the threats that are remembered. This sublinearity in the optimal amount of memory becomes stronger as the number of phage-specific CRISPR-Cas complexes necessary for an effective response, d c , increases.
Optimal amount of immune memory depends on the viral diversity. The panels show the optimal cassette size (L) relative to the number of Cas complexes ( N p ) parameterized as λ = L / N p , as a function of the viral diversity (K) relative to the number of complexes parameterized as κ = K / N p . We examine CRISPR machineries with different detection probability functions: (A) switch-like detection probability with a step function α ( d ) = θ ( d − d c ) and a smoother model α ( d ) = d h / ( d h + d c h ) with (B) h = 10 leading to nearly switch-like detection probability and (C) h = 2 leading to a softer transition between low and high detection probability. Here d c is an effective threshold on the number of complexes (d) required for detecting phages with high probability.
When phage diversity is high ( κ > 1 ), the optimal amount of memory depends on the CRISPR mechanism via the response threshold d c but is independent of viral heterogeneity so long as d c ≥ 2 (Fig. 3 see SI Appendix for discussion of the special case d c = 1 ). In nature, phages are expected to be very diverse ( K ≫ N p ). Thus, our model predicts that the cassette size of a bacterium is determined by the expression level of Cas complexes N p and the detection threshold d c of the particular CRISPR mechanism that is used by the species.
Memory Increases with Detection Efficacy.
Detection efficacy depends on two key parameters: 1) the detection threshold d c and 2) the number of available Cas proteins N p . In Fig. 4, we show that the optimal cassette size for defending against a diverse phage population ( κ = K / N p ≫ 1 ) decays as a power law of the detection threshold ∼ ( d c / N p ) − β with an exponent β ≃ 1 (Materials and Methods). This decay occurs because the trasncribed spacers compete to form complexes with Cas proteins thus, having more distinct spacers effectively decreases the average number of complexes that would be specific to each infection. Thus, the smaller the cassette size, the more likely that the d c specific complexes required for an effective CRISPR response will be produced. The optimal cassette size is a compromise between this drive toward having less memory and the drive to have a defense that spans the pathogenic landscape.
Optimal amount of immune memory depends on the detection threshold. The figure shows the optimal cassette size relative to the number of Cas proteins, λ = L / N p , as a function of the threshold to detect a phage, also relative to the number of Cas proteins, d c / N p . We consider different functional forms of the detection probability: (A) step function with a sharp detection threshold α ( d ) = θ ( d − d c ) and (B and C) Hill functions, α ( d ) = d h / ( d h + d c h ) , with h = 10 in B and h = 3 in C. Phage types are taken to be 1,000-fold more numerous than the number of complexes ( κ = K / N p = 1,000 ). (Insets) Optimal cassette size scales as L = C ( d c / N p ) − β over a realistic range of values for the number of complexes and phage detection thresholds in a single bacterial cell. Best fits are shown in each case with (A) β = 0.9 and C ∼ 1 , (B) β = 1 and C ≃ 0.7 , and (C) β = 1 and C ≃ 0.8 .
If the detection probability depends sharply on the number of bound complexes with a threshold d c (Fig. 4A), to a first approximation, fewer than d c complexes bound to a specific spacer are useless, as detection remains unlikely, and larger than this number is a waste, as it would not improve detection. In this case, if the expression of the Cas protein was a deterministic process, it would be optimal to have a cassette with N p / d c spacers, each of which could be expressed and bind to exactly d c complexes, predicting that L = ( d c / N p ) − 1 . However, since gene expression is intrinsically stochastic, there would sometimes be more than d c bound complexes for a given spacer and sometimes less. This stochastic spreading, arising partly due to finite size effects, weakens the dependence of the optimal cassette size on the threshold d c , causing the exponent β and coefficient C to be slightly <1 in the optimal cassette size scaling L ∼ C ( d c / N p ) − β (Fig. 4, Insets) see Materials and Methods for a more detailed derivation. If the detection threshold is soft, the CRISPR mechanism effectiveness is less dependent on having at least d c complexes, specific to a phage. In addition, having a slightly higher number of complexes than the detection threshold can increase the detection probability. These effects combine to produce a scaling between the optimal cassette size and the detection efficacy, L ∼ ( d c / N p ) − 1 (Fig. 4 B and C).
In summary, our model predicts that a more effective CRISPR mechanism (i.e., having lower detection threshold d c or a larger number of complexes N p ) should be associated with a greater amount of immune memory.
Our model also provides an estimate for typical number of spacers per cell in bacterial populations countering a diverse set of pathogens (regime of K ≫ N p ). Assume that the typical number of Cas complexes is N p ∼ 1,000 , comparable to the copy number of other proteins in a bacterial cell (40, 41), and that rapid detection of an infecting phage requires a modest number of activated CRISPR-Cas complexes, with a detection threshold in range of d c ∼ 10 to 100 (32). Our model then predicts that the optimal immune repertoire should lie in the range of L ∼ 10 to 100 , consistent with empirical observations (2, 7 ⇓ ⇓ ⇓ –11).
Optimal Memory with Multiple Specific Spacers.
CRISPR cassettes can contain more than one spacer specific to a given virus. This can happen, for example, due to priming, where the presence of some spacers that at least partially match an invading phage can lead to acquisition of additional spacers (23 ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ –30), increasing the effectiveness of the CRISPR-Cas system against recurrent or high-abundance viruses. On the other hand, having just a few spacers from a given phage type seems largely sufficient to neutralize reinfections (6, 8, 16 ⇓ ⇓ –19), even from coevolving viruses. Furthermore, experimentally, wild-type CRISPR cassettes are known to target diverse phages rather than mostly having spacers targeting a few threats.
Thus, we generalize our model to allow 1 < s < s max spacers to be acquired from each phage type (details in SI Appendix). Following refs. 6, 8, and 16 ⇓ ⇓ –19, the parameter s max is expected to be a small number of the order of a handful. In this case, the optimal number of spacers remains similar to the result described above when there was at most one spacer for each phage (SI Appendix, Figs. S3 and S4). For completeness, we also tested the effects of allowing s max to be unbounded. In this case, when phage species diversity is high as expected (15), the results are again unchanged. This is because having many spacers from one phage type comes at the cost of not detecting some other phage type when the number of complexes is limited by N p . If phage diversity is sufficiently low, having many spacers for each virus does not exclude any unique phage type from being well represented hence, the immune repertoire size in this scenario is not constrained.
A New CRISPR Tool Flips Genes On and Off Like a Light Switch
The classic version of the gene editing wunderkind literally slices a gene to bits just to turn it off. It’s effective, yes. But it’s like putting an electrical wire through a paper shredder to turn off a misbehaving light bulb. Once the wires are cut, there’s no going back.
Why not add a light switch instead?
This month, a team from the University of California, San Francisco (UCSF) reimagined CRISPR to do just that. Rather than directly acting on genes—irrevocably dicing away or swapping genetic letters—the new CRISPR variant targets the biological machinery that naturally turns genes on or off.
Translation? CRISPR can now “flip a light switch” to control genes—without ever touching them directly. It gets better. The new tool, CRISPRoff, can cause a gene to stay silent for hundreds of generations, even when its host cells morph from stem cells into more mature cells, such as neurons. Once the “sleeping beauty” genes are ready to wake up, a complementary tool, CRISPRon, flips the light switch back on.
This new technology “changes the game so now you’re basically writing a change [into genes] that is passed down,” said author Dr. Luke Gilbert. “In some ways we can learn to create a version 2.0 of CRISPR-Cas9 that is safer and just as effective.”
You’re Kidding. How?
The crux is something called epigenetics. It’s a whole system of chemicals and proteins that controls whether a gene is turned on or off.
If that sounds confusing, let’s start with what genes actually look like inside a cell and how they turn on. By “turning on,” I mean that genes are made into proteins—the stuff that builds our physical form, controls our metabolism, and makes us tick along as living, breathing humans.
Genes are embedded inside DNA chains that wrap very tightly around a core protein—kind of like bacon-wrapped asparagus. For genes to turn on, the first step is that they need a bunch of proteins to gently yank the DNA chain off the “asparagus,” so that the genes are now free-floating inside their cellular space capsule, called the nucleus.
Once that chunk of bacon-y DNA is free, more proteins rush over to grab onto the gene. They’ll then roll down the gene’s nucleotides (A, T, C, and G) like a lawn mower. Instead of mulch, however, this biological “machine” spews out a messenger that tells the cell to start making proteins—mRNAs. (Yup, the same stuff that makes some of our Covid-19 vaccines.) mRNA directs our cells’ protein factory to start production, and voilà, that gene is now turned on!
Anything that disrupts this process nukes the gene’s ability to turn into proteins, essentially shutting it off. It’s enormously powerful—because one single epigenetic machine can control hundreds or thousands of genes. It’s a master light switch for the genome.
The Genetic Memory Writer
The team started with a CRISPR system that has a neutered Cas9. This means that the protein normally involved in cutting a gene, Cas9, can no longer snip DNA, even when tethered to the correct spot by the other component, the guide RNA “bloodhound.” They then tacked on a protein that’s involved in switching off genes to this version of CRISPR.
Here’s the clever part: the protein is designed to hijack a natural epigenetic process for switching genes off. Genes are often shut down through a natural process called “methylation.” Normally, the process is transient and reversible on a gene. CRISPRoff commandeers this process, in turn shutting down any targeted gene but for a far longer period of time—without physically ripping the gene apart.
Thanks to epigenetics’ “enhancing” power, CRISPRoff lets researchers go big. In one experiment targeting over 20,000 genes inside immortalized human kidney cells with CRISPRoff, the team was able to reliably shut those genes off.
Not satisfied with a one-way street, the team next engineered a similar CRISPR variant, with a different epigenetics-related protein, dubbed CRISPRon. In cells inside petri dishes, CRISPRon was able to override CRISPRoff, and in turn, flip the genes back on.
“We now have a simple tool that can silence the vast majority of genes,” said study author Dr. Jonathan Weissman. “We can do this for multiple genes at the same time without any DNA damage… and in a way that can be reversed.”
Go the Distance
Even crazier, the off switch lasted through generations. When the team turned off a gene related to the immune system, it persisted for 15 months—after about 450 cellular generations.
The edits also lasted through a fundamental transformation, that is, a cell’s journey from an induced pluripotent stem cell (iPSC) to a neuron. iPSCs often start as skin cells, and are rejuvenated into stem cells through a chemical bath, when they then take a second voyage to become neurons. This process often wipes away epigenetic changes. But to the authors’ surprise, CRISPRoff’s influence remained through the transformations. In one experiment, the team found that shutting off a gene related to Alzheimer’s in iPSCs also reduced the amount of subsequently encoded toxic proteins in the resulting neurons.
“What we showed is that this is a viable strategy for silencing Tau and preventing that protein from being expressed,” said Weissman, highlighting just one way CRISPRoff—and controlling the epigenome in general—can alter medicine.
This isn’t the first time someone’s tried to target the epigenome with CRISPR. The same team previously experimented with another set of CRISPR variants that tried the same thing. The difference between the two is time and stability. With the previous setup, scientists struggled to keep the “light switch” off for a single generation. The new one has no trouble maintaining any changes through multiple divisions—and transformations—in the genome.
A reliable CRISPR tool for epigenetics is insanely powerful. Although we have drugs that work in similar ways, they’re far less accurate and come with a dose of side effects. For now, however, CRISPRoff and CRISPRon only work in cells in petri dishes, and the next step towards genomic supremacy would be to ensure they work in living beings.
If that’s the case, it could change genetic editing forever. From reprogramming biological circuits in synthetic biology to hijacking or reversing ones to prevent disease, epigenetic reprogramming offers a way to do it all without ever touching a gene, nixing the threat of mutations—while leading to lasting effects through generations.
“I think our tool really allows us to begin to study the mechanism of heritability, especially epigenetic heritability, which is a huge question in the biomedical sciences,” said study author Dr. James Nuñez.
Variability in the durability of CRISPR-Cas immunity
The durability of host resistance is challenged by the ability of pathogens to escape the defence of their hosts. Understanding the variability in the durability of host resistance is of paramount importance for designing more effective control strategies against infectious diseases. Here, we study the durability of various clustered regularly interspaced short palindromic repeats-Cas (CRISPR-Cas) alleles of the bacteria Streptococcus thermophilus against lytic phages. We found substantial variability in durability among different resistant bacteria. Since the escape of the phage is driven by a mutation in the phage sequence targeted by CRISPR-Cas, we explored the fitness costs associated with these escape mutations. We found that, on average, escape mutations decrease the fitness of the phage. Yet, the magnitude of this fitness cost does not predict the durability of CRISPR-Cas immunity. We contend that this variability in the durability of resistance may be because of variations in phage mutation rate or in the proportion of lethal mutations across the phage genome. These results have important implications on the coevolutionary dynamics between bacteria and phages and for the optimal deployment of resistance strategies against pathogens and pests. Understanding the durability of CRISPR-Cas immunity may also help develop more effective gene-drive strategies based on CRISPR-Cas9 technology.
This article is part of a discussion meeting issue ‘The ecology and evolution of prokaryotic CRISPR-Cas adaptive immune systems’.
Public health and agriculture are constantly challenged by the spread of infectious diseases. An arsenal of various prophylactic and therapeutic strategies has been developed to limit the circulation of pathogens (e.g. introgression of resistance genes in plant varieties, use of antimicrobial drugs). Yet, the efficacy of those interventions can be rapidly eroded by the evolution of pathogen populations [1–4]. It is important to note that distinct defence strategies may lead to very different evolutionary outcomes. For instance, imperfect immunity is known to select for more aggressiveness and virulence in pathogens [5,6]. In addition, distinct defence strategies may differ in their level of durability. Why are some host defence strategies overcome very rapidly while others remain effective for a long period of time [4,7,8]? A better understanding of the durability of host defences (defined as the inverse of the speed of pathogen adaptation to those defences) is key for the development of sustainable management strategies of pathogens and pests [7,9].
Empirical and experimental studies in plant pathosystems have played key roles in the identification of major factors acting on the durability of host resistance [4,7,9–11]. For instance, the type of plant resistance is known to have a significant impact on the speed of pathogen adaptation. Qualitative resistance, an all-or-nothing response, is often considered to be less durable than quantitative resistance, which reduces disease progression in the plant. This effect is usually attributed to the simpler genetic determinism of pathogen adaptation to qualitative resistance which involves a few (or even a single) major virulence genes . By contrast, adaptation to the polygenic determinism of quantitative resistance requires multiple pathogen mutations [13,14]. Yet, qualitative resistance exhibits much variation in durability . A classical explanation for this variation in durability involves selective constraints acting on the pathogen population. More specifically, host defence is likely to be more durable if the mutations (virulence alleles) that allow the pathogen to escape qualitative resistance are associated with fitness costs [4,12]. Understanding the selective constraints acting on the sites targeted by different resistance mechanisms may help predict the durability of resistance and limit the speed of pathogen adaptation [4,15]. Testing this hypothesis, however, is often difficult in plant pathosystems where measuring the durability of specific resistance mechanisms in controlled experiments raises practical difficulties [16,17].
Here, we use the interaction between bacteria and their lytic bacteriophages (or phages) to study the factors that modulate the durability of host resistance. Bacteria have access to a wide range of defence systems to defend themselves against phages [18–21]. Among these distinct defence systems, clustered regularly interspaced short palindromic repeats—CRISPR associated genes CRISPR-Cas has the unique ability to generate hundreds of different alleles of resistance targeting different sites in the phage genome . Here, we exploit this unique property to explore the variability in durability among distinct CRISPR-Cas resistance alleles targeting the same phage. CRISPR-Cas is an adaptive prokaryotic immune defence which integrates into the CRISPR locus (integration of a spacer) a small phage-DNA sequence (the protospacer, here 30 bp long) from an invading genome and uses this memory to target and degrade subsequent invading matching DNA (interference) . To select and integrate a specific protospacer from a foreign nucleic acid into its CRISPR array, many CRISPR-Cas systems rely on a 2–5 bp sequence, the protospacer adjacent motif (PAM) , flanking one side of the protospacer sequence and mandatory for spacer integration and interference. Given its size, the PAM is present numerous times on the phage genome, leading potentially to hundreds of different resistances targeting various protospacers .
To study the variability in the durability of CRISPR resistance, we monitored the evolution of the virulent phage 2972 against the CRISPR1 array of Streptococcus thermophilus DGCC7710. Streptococcus thermophilus is a Gram-positive bacterium, widely used in the dairy industry for the making of yogurts and cheese. The immunity of this bacteria against phages relies mainly on two active type II-A CRISPR-Cas systems, CRISPR1 being the more active . When a spacer targeting phage 2972 is acquired, CRISPR immunity blocks the lytic cycle of the targeted phage [23,25] and can thus be viewed as a very specific form of qualitative resistance. In this system, phages can only escape CRISPR-Cas by mutating their PAM or seed sequence (i.e. the proximal part of the protospacer) . In the following, we first quantified the ability of phage 2972 to escape a set of resistant bacteria, each of them having in their CRISPR1 array a distinct new spacer targeting a unique single protospacer site in the phage genome. Second, we isolated escape phage mutants on each of the resistant bacteria (e.g. each phage escape is mutated at a specific and different protospacer region) and we characterized their relative fitness during the infection of a population of phage-sensitive bacteria. This experimental protocol allowed us to discuss the potential link between the fitness effects of escape mutations in the phage and the durability of different resistance alleles in the bacteria.
2. Material and methods
(a) Bacterial strains and phages
The bacterium S. thermophilus DGCC 7710 (WT) and its virulent phage 2972 were obtained from the Félix d’Hérelle Reference Center for Bacterial Viruses (www.phage.ulaval.ca) . Bacteria were grown in LM17 broth (M17 Oxoid (37 g l −1 ) with 5 g l −1 of lactose) and incubated at 40 ° C. For phage amplification 10 mM of sterile CaCl2 were added to the broth. Using a standardized protocol described in , a culture of S. thermophilus DGCC 7710 was challenged with the virulent phage 2972, and the surviving colonies/cells (bacteriophage insensitive mutants (BIMs)) were screened by polymerase chain reaction (PCR) for expansion of their CRISPR array, followed by a 2% agarose electrophoresis. Primer sequences and PCR protocol can be found in the electronic supplementary material, S1. To confirm that each BIM possesses a different spacer, we sequenced the newly acquired spacers (Sanger sequencing by Eurofins Genomics MWG). A total of 17 different BIMs, each with a single and distinct spacer acquired into the active CRISPR1 locus (we checked that no other spacer was acquired on the other active CRISPR array), were kept and used in this study. Spacer sequences are provided in the electronic supplementary material, S2. Finally, protospacers were positioned on the genome of phage 2972 that is published in .
(b) Phage detection and titration
Bacterial lawns were produced by plating 6 ml of soft agar (LM17 + CaCl2 with 0.8% agar and 400 μl of bacteria in mid-exponential phase) on top of plates previously poured with 30 ml of hard agar (LM17 + CaCl2 with 1.5% agar). For phage titration, 50 μl of diluted phages were added to soft agar. For phage detection, 5 μl of phage solution were spotted directly on the solidified soft agar. When needed, phages were diluted in phage buffer (50 mM Tris–HCl pH 7.5 + 100 mM NaCl + 8 mM MgSO4). Plates were incubated overnight at 40°C and plaques were counted (titration) or recorded (detection).
(c) Durability of resistance
The durability of host resistance is defined as the time during which it remains effective . In the absence of a pre-existing escape parasite, the durability of a resistance depends on two factors: (i) the rate at which escape mutants are generated, and (ii) their spread into the host population. In the case of an homogeneous resistant host population, any escape mutant will spread quickly into the population. In this simple case, the durability of a resistance depends mainly on the rate at which viable escape mutants appear by mutation.
We used a three-step Luria–Delbrück protocol to measure the phage mutation rate for each targeted sequence by the 17 different BIM (see the electronic supplementary material, S3 for a graphic overview of the protocol). These measurements were replicated three times with three independent clonal lysates of phage 2972. To evaluate the potential influence of standing genetic variance on the rate of escape, we measured the initial frequency of escape mutants against each BIM. The frequency of pre-existing mutants to each of the 17 different BIMs was found to be below 2.9 × 10 −5 . Because we inoculated a small quantity of phage 2972 (see below), the impact of the standing genetic variance on the adaptation of the phage was assumed to be negligible.
In the first step of this protocol, for each BIM, wild-type (WT) phages were amplified in 96 independent replicates on the WT-phage-sensitive bacteria (i.e. in the absence of selection). In each replicate, 20 μl of LM17 + CaCl2 were inoculated with 0.2 μl of WT bacteria in mid-exponential phase, phages at a concentration of 300 plaque forming units (PFU)/20 μl and incubated at 40°C for 24 h. We confirmed by titrating four replicates before incubation that Ni ≈ 300 PFU/20 μl and we measured Nf by titrating 10 randomly chosen lysates. We found Nf ≈ 1.72 × 10 6 PFU/20 μl.
In the second step of the protocol, the bacteria from each replicate were pelleted down with a 5 min centrifugation (6189 g) (see the electronic supplementary material, S4) and 25% (5 μl) of the supernatant was inoculated into a 200 μl culture of the focal BIM and incubated for 24 h at 40°C. This second step ensured that even in replicates where the frequency of escape mutants was small at the end of the first step, the frequency of escape mutants would be sufficiently high to be detectable in the third step of the protocol.
In the final and third step of the protocol, the presence of escape phages in each individual replicate was assessed using phage detection assays. PE, the probability of escape, was calculated as the fraction of replicates where phage escape was detectable. It is possible  to estimate the rate of escape mutations against each BIM using:
(d) Relative fitness of phage escape mutants
For each of the 17 different BIMs, we selected at random five phage isolates that escaped bacterial resistance. A single plaque from each of these five isolates was amplified in liquid and re-isolated twice on plates, on the BIM on which they were isolated from. After amplification, phages and remaining bacteria were separated by filtration (0.2 μm) and phages were stored in 20% glycerol at −80°C. Genome sequencing (see the electronic supplementary material, S5 for the list of primers and S6 for their protospacer sequence) confirmed that all escape phages contained mutations in their PAM or their seed sequence. This protocol generated a collection of escape mutants for all BIMs.
The relative fitness of all the escape mutants was determined using triplicate competition experiments against a reference phage which contains a 37 bp deletion in its orf24 (see the electronic supplementary material, S7). This deletion allowed us to readily distinguish the reference strain from all the other escape mutants (see the electronic supplementary material, S7). Approximately 3000 phages (50% escape mutant and 50% reference phage) were inoculated in 10 ml LM17 + CaCl2 supplemented with 100 μl of WT bacteria in early stationary phase. After a 24 h incubation at 40°C, the remaining bacteria were removed by filtration and phages were stored at −80°C. Before and after amplification, the proportion of the tested phage was measured by quantitative PCR (qPCR) (see the electronic supplementary material, S7). The relative fitness of the escape mutant m was determined using
(e) Statistical analyses
All statistical analyses were run using R software (v. 3.3.2, ), through RS tudio (v. 1.0.136).
We performed an analysis of variance (ANOVA) to determine if the position of the protospacer on the phage genome impacts the durability of resistances.
Linear models were used for the analysis of relative fitness data. In the first model, we tested the effect of phage genotype on relative fitness. To control the false discovery rate, as all escape mutations were compared to the reference phage independently, the Benjamini-Hochberg procedure was applied to the derived p-values. In the second model, we tested the impact of mutation type (synonymous versus non-synonymous) on relative fitness of phages escaping the BIMs that target an orf (36 phage escape mutants). In the third model, we assessed the effect of the relative fitness of phage escape mutants on the durability of resistance of their respective BIM.
To study the durability of various CRISPR alleles, we generated 17 different resistant strains (BIMs) characterized by a new and unique spacer within the CRISPR1 array. Each spacer targets a different protospacer, i.e. a different part of the phage genome (electronic supplementary material, S2). In total, 13 of the 44 phage genes (as well as some non-coding regions) were targeted by at least one spacer, leading to a good coverage of the phage genome by these 17 BIMs (figure 1).
Figure 1. Variability in the durability of CRISPR-Cas immunity. The mutation rate of 17 protospacers, whose positions are labelled on the x-axis, were measured using fluctuation tests. PE values, i.e. the number of replicates in which a phage escape mutant evolved, are reported and show heterogeneity among the targeted sequences, implying that there is heterogeneity in the durability of CRISPR-Cas resistances.
Our measures of BIMs’ durability using fluctuation tests, revealed considerable variation in the ability of the phage to escape different BIMs (figure 1 electronic supplementary material, S8, ANOVA, F16 = 10.89, p = < 0.001). We also used the probability of escape to estimate the mutation rates for each target sequence (seed and PAM sequences) (see the electronic supplementary material, S9). The average mutation rate was estimated to be 3.4 × 10 −7 mutation/target sequence/replication and the rate of escape on the less durable BIM was 123 times higher than one of the most durable BIM.
One possible explanation for the observed variation in durability of resistances could result from differences in the fitness costs associated with their respective escape mutants. In principle, the use of the proportion of replicates with no escape mutants in the Luria–Delbrück protocol yields an estimation of the mutation rate that is unaffected by the selection on viable mutants . To explore the validity of this hypothesis, we isolated 40 phage mutants escaping the 17 distinct single CRISPR-resistances. A total of 35 escape phages carry a single bp mutation in the targeted sequence, four of the remaining phages carry double bp mutations in the targeted sequence, one escape phage has a single bp deletion (see the electronic supplementary material, S6). Among the substitutions, 27 are transversions, 12 are purine transitions and four pyrimidine transitions. Ten escape mutants were characterized by synonymous mutations (see the electronic supplementary material, S10).
To measure fitness, we competed each of the escape phage mutants against a reference phage and measured their relative abundance before and after the experiment. From these data, we deduced relative fitness. We found that relative fitness was highly variable, ranging from −6.21 to 0.68 with an average of −2.22 and a standard deviation of 1.71 (figure 2 see the electronic supplementary material, S11). Although the majority of the phage escape mutants had a lower fitness than the WT phage (32 out of 40), some escape mutants were neutral (8 out of 40) (figure 2 electronic supplementary material, S11). The presence of non-synonymous mutations was not a good predictor of escape mutant fitness (t = −0.509, P(R > t) = 0.612) and all tested synonymous mutations but one lower phage fitness (see the electronic supplementary material, S12). We found that escape mutant fitness was not a good predictor of the durability of each BIM (figure 3, t = −0.423, P(R > t) = 0.673). Hence, the heterogeneity in the durability of CRISPR resistances is not caused by the heterogeneity of fitness costs associated with these escape mutations (figure 3).
Figure 2. Distribution of fitness effects of escape mutations in the phage. Relative fitness was measured through competition experiments with a collection of 40 escape phages, mutated on their seed or PAM sequences. Phages that carry a neutral and deleterious mutations are represented in medium and dark grey, respectively. Black dots show the relative fitness of each escape phage. The dotted segment represents the fitness of WT phage 2972. Fitness value of each escape phage is also provided in the electronic supplementary material, S11.
Figure 3. Relative fitness of phage escape mutants against durability (probability of escape PE) of their respective BIM. Each colour corresponds to a single BIM and each dot to a single escape phage. Error bars correspond to 95% confidence Intervals. Raw data are provided in the electronic supplementary material, S9 and S11.
We studied the variation in the ability of the virulent phage 2972 to escape distinct resistance alleles at the CRISPR-Cas immune system of its host S. thermophilus DGCC7710. We found (i) considerable variation in the durability among these different resistant strains (and therefore in the apparent mutation rate of phage protospacers), and (ii) substantial variation in fitness among phages carrying escape mutations. Yet, the cost of those escape mutations was not associated with the durability of their respective resistance strains. If the fitness cost of escape mutations is not a good predictor of resistance durability what drives the variation in durability? We believe that two non-mutually exclusive processes could explain the observed patterns: (i) variation in the mutation rate along the phage genome, and (ii) variation in the probability of generating lethal mutations among different sequences targeted by the CRISPR-Cas system.
First, a variation in the mutation rate along the phage genome can result from a heterogeneity of the replication machinery. Such a variation in mutation rates has previously been described in yeast , RNA viruses  and bacteria  but to our knowledge not yet in bacteriophages. The precise mechanism used by phage 2972 to replicate and repair its genome is unknown, limiting our ability to test this hypothesis. However, since phage 2972 encodes and expresses its own replication machinery and does not possess any repair mechanism [27,35], it is tempting to hypothesize that no repair mechanisms are involved and that the entire replication is made by its replication machinery. This machinery could yield substantial variation among different parts of the phage genome. Note, however, that most escape mutants we isolated were owing to transversions instead of transitions (see the electronic supplementary material, S10), whereas most replication machineries show a biased pattern to transition [32,36]. If a heterogeneous fidelity rate was at the origin of the observed heterogeneity in the durability of resistances, 2972 machinery would have an unconventional mutation bias.
Second, variation in the frequency of lethal mutations along the phage genome could also contribute to the observed variation in BIM durability. Lethal mutations are very common and can reach up to 40% of viruses total mutations [37–39], but, to our knowledge, the heterogeneity of the probability of lethal mutation along the genome has not been studied. Because some genes are known to be essential while others are accessory (e.g. orf39 and orf41 are not expressed during an infection by phage 2972 ), we can expect that mutations in different genes should result in different fractions of lethal mutations and, consequently, in variations in the durability among BIMs targeting these different genes.
Additional experiments are required to evaluate the relative importance of the variations in (i) mutation rate and (ii) the proportion of lethals along the phage genome on the durability of CRISPR resistance. The heterogeneity in the mutation rate could be assessed by measuring the durability of several spacers that target different non-functional coding regions of the phage genome. Phage 2972 carry such a sequence in the form of an incomplete lysogeny module that is not expressed [27,35]. If we could create different BIMs targeting this module, any heterogeneity in durability among those BIMs would only result from an heterogeneity in the mutation rates among the different target sequences. To evaluate the alternative hypothesis that the variation in durability results from variation in the fraction of lethal mutants, one could measure directly this fraction of lethal mutants through the systematic introduction of point mutations in the target sequence of BIMs with contrasted levels of durability [37–39]. Thanks to recent progress in molecular biology, a range of mutants can be produced by systematically changing each of the nucleotides of the target sequence [40,41]. The comparison of the number of lethal mutations for a durable and non-durable resistance would allow one to evaluate directly the impact of this factor on the variation of the durability.
CRISPR-Cas immunity is known to generate and maintain a high diversity of resistance alleles against the same phage [22,42] and this diversity in resistance is known to limit the growth of the phage population [29,42]. Theoretical models and experimental tests indicate that such diversity limits the evolutionary emergence of the pathogens . Yet, those studies ignore the heterogeneity in the durability of resistance among different alleles. Our results indicate that another potential benefit of generating this diversity is to explore a range of durability of resistance. The most durable alleles will outcompete the other BIMs and this may provide a very robust way to hamper the evolution of the phage. In addition to this inter-host diversity, a single cell can acquire more than one spacer against the same parasite. The acquisition of multiple spacers targeting different parts of the phage genome implies that the phage needs multiple mutations before it can infect this multiply resistant bacteria . As most escape mutations are costly (figure 2), carrying multiple escape mutations is likely to reduce dramatically the fitness of the phage. By contrast, the acquisition of multiple spacers does not alter the fitness of the bacteria . This asymmetry may help in explaining the ultimate extinction of phage populations coevolving with CRISPR-Cas immunity [22,44]. It is also important to note that some phages have evolved the ability to defeat CRISPR immunity using anti-CRISPR proteins that inhibit the defence conferred by CRISPR-Cas [45,46] (note that to our knowledge, phage 2972 does not carry any anti-CRISPR against S. thermophilus CRISPR systems). Even though anti-CRISPR can be partially efficient against CRISPR-Cas, the cooperation between phages ensures that, above a minimal concentration, phages can invade a resistant host population without acquiring escape mutations in the sequences targeted by CRISPR-Cas [47,48].
Streptococcus thermophilus is widely used by the dairy industry for the manufacture of several fermented milk products (yoghurt, cheese) and the identification of BIMs with particularly durable resistance could have very practical implications. The use and/or the combination of these BIMs is likely to protect the starter cultures against phage infection. In addition, it would be particularly useful to identify durable spacers that target related phages. Such generalist spacers have been observed before . The use of a durable generalist spacer could massively improve the resistance of S. thermophilus strains. Our biological model also provides a unique opportunity to evaluate experimentally the effectiveness of different intervention strategies on the long-term efficacy of resistance to pathogens. It may thus provide important insights for the implementation of sustainable management of pathogens and pests [4,9,29].
In addition to these applications in the dairy industry and in agriculture, the CRISPR-Cas9 technology can be used as a driving endonuclease, ie. a genetic tool that makes an engineered allele spread into natural populations by non-mendalian heredity . Indeed, in a heterozygote carrying a CRISPR-Cas9 and its guide, the endonuclease will target and cleave the homologous allele. As repair mechanisms usually involve homologous DNA sequences, they will usually add a copy of the CRISPR-Cas9 and its guide at the place of the former allele, leading to the rapid spread of the CRISPR-Cas9/guide in the population [49,50]. However, if the presence of CRISPR-Cas9 is costly for its host, it is likely that escape mutation will emerge and break the spread of the gene-drive [50,51]. Our results indicate that the durability of gene-drive strategies targeting distinct genome regions is likely to be very variable. Understanding the ultimate source of the variation of durability is particularly important for the effectiveness of gene-drive based on CRISPR-Cas9.
Designer Cas9 proteins
Cas9-sgRNA ‘off-the-peg’ catalyses R-loop interference reactions triggering a DDSB at the site of the R-loop. This has also facilitated development of gene editing technologies based on modifying the protein architecture of wild-type Cas9, nuclease inactivated Cas9 (dCas9) and single-strand cutting ‘nickase’ Cas9 (nCas9) including transcriptional regulation and imaging, reviewed recently in . Cas9 protein fusions have also been generated to enhance HR in human cells, by biasing DNA repair pathway choice at the site of the DDSB. Two examples have fused CtIP and RAD52 to Cas9 [82,83].
The CtIP fusion protein was explored using two different methods. An active Cas9-CtIP fusion was able to stimulate an increase in editing compared with standard HR, and a second Cas9-fusion enhanced HR further by fusing an N-terminal fragment of CtIP, deemed the HR-enhancer domain (HE), that is crucial to its initiation of HR . The RAD52 fusion protein was designed with the same rationale of forcing a protein crucial to the completion of HR close to the site of a Cas9 DDSB and was found to enhance the efficiency of reporter cassette insertion .
Fusions of dCas9 or nCas9 to DNA base modifying enzymes have facilitated editing of single bases. A cytidine deaminase (CDA)—dCas9 fusion has generated a C to T transition at R-loop targeted cytosine residues by generation of uracil, which is replaced with thymine during subsequent DNA repair (Figure 3) . The R-loop generated window of ssDNA allows the deaminase to convert C into U. By fusing uracil glycosylase inhibitor (UGI) to the C-terminus of CDA—dCas9 base-excision repair was prevented, allowing mismatch repair (MMR) to complete the C to T change . The system was enhanced by fusion of a second UGI, to further favour MMR, and the Gam protein derived from bacteriophage, which binds the free ends of DDSBs, minimising In/Del generation. An adenine deaminase fused to dCas9 has been effective at targeted conversion of adenine into inosine, which is in turn converted into guanine . A similar system, RNA Editing for Programmable A to I Replacement (REPAIR), has also been reported for the single-base editing of adenosine to guanine through an inosine intermediate in RNA transcripts in mammalian cells utilising catalytically inactive Cas13, a class 2 CRISPR-Cas RNA editing enzyme . Finally, the reliance of HR-based CRISPR-Cas9 genome editing on host cell HR enzymes might make it attractive to develop a Cas9 fusion to proteins that are active as site-specific recombinases or have similar DNA integration activity (Figure 3). Cas9-mediated R-loop formation would in this scenario target DNA for integration of a duplex DNA payload carried by the fusion enzyme.