**Conflict of interest**

The author declares that he has no conflict of interest.

*Nonlinear Dynamics Phenomenon in a Polydyne Cam with an Offset Flat Faced Follower… DOI: http://dx.doi.org/10.5772/intechopen.106179*


*3.2.2 Wear (Abrasive wear) and friction mechanism of bare and APS-coated specimens against steel*

Morphological structure and hardness are the two main parameters affecting the wear resistance of any coatings. **Figure 10a** and **b** represents the micrograph image of the wear mechanism of the Ti sample during uniform directional sliding of HA-C coated specimen. SEM micrograph after wear test exposure shows uniform and continuous wear. It can be observed that wear tracks are visible due to abrasive

*Tribological Behavior of Atmospheric Plasma Sprayed HA-CNT Coatings of Biomaterials DOI: http://dx.doi.org/10.5772/intechopen.103860*

**Table 4.**

*Result of Tafel polarization test of reinforced HA-C coating on SS 316L, CoCrMo and TTTi6Al4V in SBF solution.*

**Figure 10.**

*Wear scar micrograph of APS- sprayed HA-C coatings against steel ball (a- wear mechanism & b- wear path).*

action on the both uncoated and coated surface. The micrograph also shows the small debris by abrasion. SEM images revealed the presence of areas of cracks and fractures due to plastic deformation, which is attributed to abrasive wear [40]. In the HA-CNT wear track, the image depicts smaller craters and a huge area of abraded surface. Fracture and chipping form the crater, while abrasive wear results in the rough surface.

Average Weight loss (kg/m2 ) of uncoated samples was 0.14, 0.12 and 0.09 kg/m2 respectively whereas for reinforced HA-C coating it was 0.24, 0.22 and 0.18 kg/m2 respectively. It may be observed from the wear results that weight loss of metallic substrates is comparatively lower than that of all as-sprayed coatings on alloy substrates. It can be observed from the plotted results from **Figure 11** that, the wear volume was reduced up to 75% with the improvement in the wear resistance of reinforced HA-C coatings with 10 wt% and 5 wt% reinforcement. This improvement is because of the increased fracture toughness and elastic modulus.

The average wear rate and friction coefficient fall into a near mild wear regime, with values ranging from 10 to 7 to 10–4 mm3 /Nm [41]. The tribological test parameters were chosen with the conditions of medical implants working inside the human body, specifically the failure of the femoral head within the acetabular cup of the hip joint. Normally 0.8–2.5 MPa of stress is considered in a hip joint while walking [42]. The HA-C coating has to withstand a maximum frictional force for a minimum period of 20 years [43]. To compensate the entire working life a high value of 10 N load is kept during the entire test.

Surface hardness, toughness, spraying technique, and contact pressure are considered as the significant effecting factors on the coefficient of friction value. **Figure 12** represents the accumulated mean CoF from 0.35 to 0.42 due to the

#### **Figure 11.**

*Variation in wear loss (kg/m2 ) of uncoated, 10 wt% and 5 wt% HA-C coatings alloy in dry and wet (SBF) condition.*

#### **Figure 12.**

*Average CoF curves for APS coating against steel ball SAE 52100 at contact pressure of 2000 MPa. (a- sliding distance vs CoF & b- time vs CoF curve).*

varying contact pressure of the steel ball. The effect of the spray technique is the drop in mean CoF up to 0.37 for the Ti coated samples in wet (SBF) conditions. The total mass loss study demonstrated that the presence of PBS in the electrolyte solution tended to exacerbate the deterioration of the alloy under the circumstances examined, which was also verified by the 3D analysis of the wear track geometry [44]. As hardness of counter body governs the tribological failure, so it primarily influences the specific wear rate. Ti6Al4V alloy has shown maximum wear resistance among all uncoated and coated specimens. Improvement in wear resistance may be observed with the incorporation of reinforcement in the case of HA coatings. There is a slight decrease in CoF from 0.8 to 0. 6 with the addition of reinforced CNT into the HA matrix. As the graphene layer is peeled off from the CNT surface it offers lubrication causing the decrement in CoF value.

A tensile force ≥11 GPa is required to remove the single graphite layer from multiwalled CNT along its axial direction [39]. The evaluated tensile stress in the wear track in the current study was ~12 GPa, which was found to be sufficient for the removal of

*Tribological Behavior of Atmospheric Plasma Sprayed HA-CNT Coatings of Biomaterials DOI: http://dx.doi.org/10.5772/intechopen.103860*

the graphene layer from CNT. Similar results were seen when the test was carried out under SBF immersed solution. As the body fluid would offer additional lubrication on the implant surface it would decrease the amount of wear debris thus, expecting better performance of coating inside the human body. Tribo Mechanical wear is the most common wear mechanism, as seen by abrasion, adhesion, and cracking [45].

**Figure 13** represents the wear depth profile and 3D topography of HA-C coating against SAE 52100 steel. The wear volume against the track was calculated (using average value of cross-section area from three variable experimental sets with track diameter) from the profile and found to be 0.25 mm3 for HA-CNT coating. With CNT addition it decreases up to 75% resulting in a "reduced probability of disturbance in the biological environment around the implant". The 15.70 μm of wear depth is recorded which is maximum for both types of coatings versus a steel ball.

#### **3.3 Bio-compatibility evaluation of HA-CNT coatings**

MTT assay was used to determine the mitochondrial activity and cellular viability of 3T3-L1 viable cells on uncoated and reinforced HA-CNT coatings. When incubated with viable cells, the reduction reaction of MTT reagent causes it to be reduced into purple formazan crystal. The cell viability is indirectly reflected by the absorbed formazan crystal. As a positive control, cells seeded on tissue culture plates are used.

#### *3.3.1 Cell viability study*

Morphology of cell-seeded on bare SS-316 L, CoCrMo, and Ti6Al4V substrates after 2 days were analyzed. Cell viability (%) is shown in the histogram presented in **Figure 14**, after 2, 15, and 30 days of incubation. It may be observed that cell viability on Ti6Al4V substrate was 72%, 81%, and 88% respectively whereas it was 68%, 78%, and 85% on CoCrMo and 66%, 78%, and 84% respectively on SS-316 L. Further, cells were comparatively less viable initially on all the three alloy substrates than that of the grown cells on the tissue-cultured plate (p,0.05), but viability increased with exposure time duration.

The morphology of 3T3-L1 cells seeded on reinforced HA-CNT with 10 wt% and 5 wt% coatings (as-sprayed) on all Ti6Al4V alloy samples is shown in micrographs presented in **Figure 15a–d**, respectively. According to the viability results, cell viability of 5 wt% reinforced HA-CNT coatings is lower than that of 10 wt%

**Figure 13.** *(a) Wear depth profile and (b) 3D topography for APS coated sample.*

**Figure 14.** *Viability of 3T3-L1 cells on Ti6Al4V, CoCrMo and SS-316L alloy from 2 to 30 days.*

**Figure 15.** *SEM (BSE) image of 3T3-L1 cells seeded on (a) control (b) bare Ti6Al4V (c) 5 wt% Ti6Al4V (d) 10 wt% Ti6Al4V.*

coatings after 2 and 15 days of exposure, respectively. However, after 30 days of exposure, the viability of all as-sprayed coatings was comparable.


devoted to overseeing California's privacy laws, and once established, may even exceed the size of the Federal Trade Commission's privacy and consumer protection division, responsible for enforcing consumer privacy protections across the entire USA.

Based on the recent examples referred to above, it is expected that the GDPR will continue to operate as a trigger for non-EU countries to adopt much higher data protection standards than they do today. This, in turn, should lead to greater upward convergence of data protection principles internationally, at both bilateral and multilateral levels—a goal which is in the interest and to the benefit of citizens and businesses alike wherever they are in the globalized world. Released from the bottle, the privacy genie isn't likely to be returned. Even companies that may not directly engage in business with the EU would be wise to be aware of the GDPR's provisions and consider proactive compliance in anticipation of continued international adoption of its core principles.

#### **18.4 Conclusions**

The protection of personal data is becoming an issue that now, more than ever, affects all organizations around the world. The global scene has changed dramatically during the last few years because of the vast technological advancements affecting every sector. To this end, the reforming of the existing regulatory schemas in order to capture the mechanisms and the technologies that are used for the processing of personal data was of utmost importance. European Commission with the GDPR aimed to define a harmonized framework of action with respect to individuals' privacy. Existing national laws were too difficult to be controlled, leaving room for derogations. After the establishment of the GDPR, other nations followed this example, either by being based on this regulation and developing their own (i.e., LGPD of Brazil), by establishing frameworks in alignment with the European regulation in order to be able to transfer personal data (i.e., Privacy Shield, between EU and USA), or by demonstrating that they have undertaken all the necessary actions,11 ensuring an adequate level of protection.

11 https://ec.europa.eu/info/law/law-topic/data-protection/data-transfers-outside-eu/adequacyprotection-personal-data-non-eu-countries\_en.

Compliance with the GDPR comprises a challenging project for organizations for a series of reasons; the complexity of business activities and the duplication of data (in different information flows or even entire departments within an organization) are the most important ones. However, even if organizations need to comply with the GDPR, they lack guidelines that could help them into reaching compliance. There are already products being developed that can be used toward this compliance; however, none of the current technical solutions is able to capture the current security status of an organization, identify the gaps, assess the criticality of the processing activities and the personal data that they use, provide concrete solutions tailored to each organization to finally fortify its processes, and guarantee the protection of individuals' personal data [31]. The "ten steps for compliance" list that is provided in this work aims to facilitate data processors/controllers toward their compliance. Of course, if organizations have already been certified under a specific certification schema (e.g., ISO 27001), they have already satisfied a part of the requirements that the GDPR requests, which means that less effort is required [32].

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chapter 19 Reflections: Bringing Privacy to Practice**

**Jennifer Romano and Liz Keneski**

**Abstract** We interviewed a panel of 13 applied researchers to understand why applied and academic privacy researchers do not collaborate more often. While many agree about the benefits of collaboration, they simply do not collaborate due to real and perceived barriers, such as timelines, goal differences, and data-sharing difficulties. We synthesize the findings and provide actionable recommendations to help bridge the gap between academic and applied research.

# **19.1 Introduction**

In our work across academic and applied settings—from research agencies to inhouse research teams, from scrappy start-ups to established organizations—we have identified a hole. Academics and applied researchers are not collaborating, and those collaborations that exist are rare. The collaborations are rare enough that we, and the editors of this book, decided to raise awareness by publishing this book. We hope you, the reader who has made it to the final chapter, agree.

One might wonder why bridging applied, or "industry," and academic privacy research is an important issue. As the other chapters in this book have demonstrated, academics are tackling big, important privacy issues. However, they are not necessarily the same big, important privacy issues that we in industry attempt to tackle. We believe it is essential to work together:

1. **In order for applied privacy researchers to utilize the foundation that privacy academics have built.** This can inform our very fast-moving applied work.

J. Romano (-)

Google, New York City, NY, USA e-mail<EMAIL_ADDRESS>
L. Keneski Facebook, Boston, MA, USA e-mail<EMAIL_ADDRESS>
2. **In order for privacy academics to understand the problems that applied industry is facing.** This can make academic research farther reaching and applicable to real-world problems.

In order to provide a broad and balanced opinion informed by diverse experiences, we believe collaboration is essential. Industry folks are moving fast and are often thinking about issues in an applied context, which sometimes varies from academic researchers. Academics, on the other hand, have the wealth of knowledge from both their own past work and that of others and the time to ponder issues at length. It is only by collaborating that we can get these diverse viewpoints in the same room.

There is a lot of distance between privacy in the books and privacy on the ground. Scholarly debate about privacy is important—it helps guide discussion.

– Trevor Hughes, International Association of Privacy Professionals

In an effort to share the landscape, we believed it was important to talk to fellow applied researchers working outside of academia and to synthesize their views alongside ours. We conducted one-on-one interviews with a panel of 13 applied researchers who have engaged in academic collaborations and/or other cross-industry research endeavors. Further, we solicited input and feedback from the Privacy Research Teams at Google and Facebook. What follows in this chapter is a synthesis based on these researchers' and our experiences, advice, and hopes for the future of privacy research across academic and applied contexts.

Our panel consisted of:

companies through her agency User Behavioristics, such as EA, Disney, Blizzard, and King.

teaching graduate students in user-centered design, design thinking, research, usability testing, and web design since 2012.

• **Trevor Hughes:** International Association of Privacy Professionals (IAPP). Trevor leads a professional association of over 70,000 privacy professionals and provides oversight of the large research and editorial agenda at IAPP.

And us!

# **19.2 Why Industry-Academic Partnerships Are Valuable**

## *19.2.1 Applied Research Benefiting from Academic Research*

Applied researchers often need to move quickly—our science is in service of informing upcoming decisions about products, programs, policies, etc., and those outcomes come with hard deadlines. However, this also means that we cannot always comprehensively investigate a given topic from "top to bottom" due to time constraints. Industry benefits from working with academics because it can create the opportunity to look at a problem more objectively and over a longer term. In order to benefit from this long-term academic thinking, the work that applied researchers and academic researchers do together needs to be scoped to occur over several years—a true long-term partnership.

The deep knowledge and experience in the specific space is so good for us. We get deeply expert in one space, but then we get moved to another space and become an expert at that. We are never deeply understanding a space like academics.

– Rebecca Destello, *Facebook*

Another way that applied researchers can benefit from academic research is via academics' expertise in working with particular topics or sensitive populations (e.g., older adults, teens and children, people experiencing cyber abuse). As discussed in Chap. 15, vulnerable populations face unique privacy risks that not only challenge designers' preconceptions about privacy but also are often overlooked in decisions about privacy design and policy. Further, existing frameworks often overlook the privacy concerns of people who experience heightened risk. Applied sectors have a vested interest in serving diverse consumers or clients well; however, that doesn't always mean that the right course of action is for applied researchers, many of whom have more generalized research experience, to reach out directly to potentially vulnerable populations for research. Rather than industry trying to break into communities, it's better to work with people who are trusted in those environments. Many times, those are the academics.

At Walmart, I studied how people budget in preparation for holiday shopping. I learned that people usually set a budget for different categories, ranging from bills and rent to savings, entertainment and "me-money." Some people used envelopes for cash for food, bills, transportation, etc. to categorize expenses. Customers part of a different segment did not use envelopes, but they also had ways to categorize and control expenses, for example, one checking account and three savings accounts—one for emergency, one for kids education, and one for monthly unexpected events like if the washer broke. People had a way of controlling their budget, whether physical or in their heads. While analyzing the data I collected, I did a quick review and learned about a solid concept in the literature, called 'mental accounting,' that described exactly what I had been seeing in my studies.

– Janaina Pantoja, *eBay*

#### *19.2.2 Academic Research Benefiting from Applied Research*

Some of the problems applied research is positioned to solve can benefit academics because of the potential for impact. Many of these problems are the exact "big questions" that motivate academics to study privacy in the first place—e.g., How should technology companies protect the privacy of potentially vulnerable people? How should governments collect census data in ways that promote necessary social programs but also responsibly collect, use, and store sensitive data? How should IoT technologies provide in-home, everyday value to people's lives while also protecting their privacy? Academics' opportunity for impact in the applied sector—to influence the experiences of billions of people's lives on a daily basis—is unparalleled.

Another way that academics can benefit from such collaborations is access to resources. Despite developing rigorous theory-derived hypotheses in academia, academic researchers sometimes lack the resources to appropriately test their hypotheses with large enough or diverse enough samples. One way that academic researchers may benefit from applied research is to capitalize on the possibility to (a) collaborate, and thus have access to larger samples (with appropriate privacy protections), and/or (b) bidirectionally replicate work with multiple samples across academic and applied settings. In addition to sheer sample size, an additional benefit of applied research is the ability to access diverse, global samples through resourceintensive methods.

# *19.2.3 How Academics Can Best Contribute to Applied Outcomes*

Until there is a direct application for academic research results and they influence our ability to build something, they simply won't be used. This means that in order for applied researchers to benefit from academic privacy research, it is paramount that academic researchers do not just imply or suggest how their work "might" or "could" be applied, but they need to take the next step to *understand* and to *collaborate with* the industries they seek to influence.

This type of impact starts with academics gaining as much context as possible about the applied sectors they seek to influence. Industry researchers do not have unlimited manpower, technology, etc., to create the solutions academics are calling for, and this lack of consideration for the business reality of solving real-world problems can make implementing academics' recommendations untenable. Academics should reach out to applied researchers and ask questions about how their work can influence applied outcomes. Then, they can incorporate that knowledge into their future research and especially into their recommendations for applied sectors.

#### *19.2.4 Successful Relationships*

Some have mastered this, and it is up to the rest of us to learn and follow suit. For example, Anja Dinhopl, a UX Research Manager at Google, shared an example of a successful partnership between applied researchers and academics. For research she conducted, she collaborated with academics for foundational work that she and her team then wanted to build on, rather than starting from scratch. She said that the applied team was "able to move much faster than most industry groups." She also argues there need to be feedback mechanisms between communities, industry researchers, and academic researchers. Ideally, this would include a way to share back to the community and a *stronger commitment* for industry to share with academia and legislatures and maybe even competitors. This can be done via white papers, collaborative meetings, conferences, published articles and blogs, and more. There is a need to give transparency of *why* we are doing what we are doing, and with that comes building on existing research. We cannot simply conduct research in a silo, but we must share with other researchers and organizations so others can learn as well.

Heather Desurvire from Google also shared an example of a successful partnership. When she conducted game user research, she saw a need for partnering on a recurring problem in many games she worked on. While she ran a research agency, she recognized that she was answering the same problems: AAA game companies were producing games that were showing the same critical player issues over and over. For example, the game tutorials were consistently too open world or too pedantic—neither are optimal for learning the tools to play the game. There was a need in industry to find a more principled way to create optimized designs. And the only way to do so properly and to have impact industry-wide was to understand the principles behind optimizing the player experience. Heather was able to offer consistent insights to all the game studios. She partnered with academics she had met at conferences, through her association with USC and a fruitful collaboration with the Microsoft games research group who were also geared toward raising the bar for all game studios via shared knowledge. Heather conducted theoretical work with authors and academics and shared it back to the game companies. Together, they came up with principles. They then had the task of actually applying them. This occurred eventually via publications in conferences and game research books, as well as teaching them to up-and-coming game designers, as a faculty member at USC's Interactive Media and Games department. An example of a play principle they developed is: (1) the game presents overarching goals early as well as shortterm goals throughout play, (2) the game does not put any unnecessary burden on the player, (3) mechanics are easy to learn but hard to master, (4) the player should not lose any hard won items, and (5) the game world reacts to the players and remembers their passage through it.

Another way Heather bridged the gap was to take a faculty position, teaching future game publishers game user research. By having future designers understand the player experience, it was a "way to plant a seed on how to optimize player experience by teaching them the concepts." To Heather, planting the seeds was a way of raising the bar. And she has been teaching now for over a decade and has seen those seeds flourish in industry. In fact, many game researchers in the gaming world, who were there since the beginning, came from academia. That bridge happened naturally in the gaming industry, as a result of this dual focus. Heather and these others helped define and pioneer the game research methods that are now industry standard.

Ideally we should work towards a multi-partnership—academia, government, non-profits, and industry. Academia is known for methods, detailed inquiry, and long-term goals. Applied research focuses on how findings impact prioritization and development of products, programs, processes, etc. Government makes the rules, but may wrestle with the right approach when designing privacy experiences is a complicated endeavor without obvious answers. Government agencies may not always trust companies to come up with design principles, but overly detailed mandates can also be problematic or restrict innovative problem-solving. While academics, policy folks, and industry researchers seem to have conflicting interests, they actually are all working toward common goals of helping people benefit from using products while protecting their privacy. Each sector differs in how they prioritize and work toward these goals. We simply have a fundamentally different way of framing things. But in the end, we are all working toward the same outcomes and can help one another get there if we work more closely together.

Companies face a variety of commercial, legal, ethical, and reputational risks that serve as disincentives to sharing data for academic research, with privacy—particularly the risk of reidentification—an intractable concern. For companies, striking the right balance between the commercial and societal value of their data, the privacy interests of their customers, and the interests of academics presents a formidable dilemma.

To help support data sharing for research by companies, we have developed model contracts, a special ethics review committee for projects not covered by IRB review and an award to honor successful industry-academic teams that collaborate using company data safely. We hope this will move the ball forward.

– Jules Polonetsky, *Future of Privacy Forum*

#### **19.3 Why These Partnerships Are Challenging**

So why don't more of us foster these relationships? Why aren't all applied researchers partnering with academic researchers? There are many reasons, but one that stands out to us and many in our panel is **timelines**. Simply put, the timelines that academic researchers and applied researchers work with are different. Applied researchers are moving quickly, sometimes conducting a project from start to finish in just a few weeks. Academic researchers, on the other hand, take much longer in order to conduct research that can lead to publication of peer-reviewed results. Projects can last anywhere from a few months to many years. This discrepancy poses a bit of a problem. It is harder to adopt what you learn in academia in industry because we need to do it fast—we aim to solve immediate problems.

In addition, because academia aims for knowledge that is generalizable and scrutable by third-party reviewers, there is a general sense that there are **so many steps** one has to go through to conduct academic research, such as IRB approvals, statistical testing, theoretical validation, and generalization rather than focused and contextual findings. Heather Desurvire from Google serves as a bridge herself, and she shared a story about her experiences working with a new field: "Academia funded the research, and the work was adopted because the field was brand new. There were no real methods in industry. We applied what we learned in academia in industry." But it was not always easy, and everything was not readily accepted. "Some work was criticized as not statistically valid, and some qualitative research was looked down upon." But she did not stop. While the early work received some initial criticism, over time the qualitative work was appreciated for its depth, and the academic partners utilized it. It is often important to use both qualitative and quantitative work together, for example, when studying gaming and the player experience: "You can look at quant, but need the qual to understand WHY. This was taking place in industry, not academia. Academia did the theoretical work, not the applied work." And if it is not something you can eventually use, why is the work being conducted at all?

Applied researchers are working on improving products, and while time and other constraints play a role, is it simply that applied researchers don't care about the theoretical work? Well, not necessarily. Gretchen Gelke started working on privacy at Google about 3 years ago. Prior to this role, she worked on security software, hardware, and smart devices—privacy and data have been key issues for her for a long time. Compared to other companies, her privacy specialty was never really the focus of her role *...* until Google, where she realized her specialty was an asset. The team she joined, while very applied, was also extremely academic. They focused on products (e.g., helping users find privacy settings) and also foundational research both across Google and externally. The team is frequently asked to present the foundational work to product teams to inform thinking and inspire change in products, overarching strategy, or to inform entirely new directions. The team hired people with PhDs in privacy and security to help inform policy and set context for the product changes being recommended. These efforts were critical for Google. Having the academics in house helped the team make *better* applied product decisions.

Unfortunately, applied research teams reinvent the wheel constantly conducting research that has likely already been conducted in academia or by previous colleagues. Sure, it would be easier to go to someone who has done the work, but we want to make sense of it too. So how do we stop reinventing the wheel and shift to doing this together?

So what do we do? Do we abort the mission of trying to bridge? Of course not; we certainly don't think so (or we would not be writing this chapter). We believe that collaborations are essential for building on existing work and applying that work to real-world problems. Academic researchers have been studying many of the things we are grappling with in applied research for years! We need to figure out how to make those timelines match up. If we have problems we are interested in examining in a year or two, it would be nice to be able to predict that so academics could get a head start. Applied researchers need to be proactive, *to predict future needs*, to move earlier to foster those collaborations.

# **19.4 Actionable Strategies for Making These Partnerships Work**

In order to properly bridge, we believe that industry researchers need to be mindful about what academic institutions are worried about, and academic researchers need to be mindful about what industry researchers are worried about. We need to make sure the collaboration is good for both sides. We believe we need to also work against some of the biases, for example, the incorrect stereotype that companies do not *actually* want collaboration, they just want to be told they are right. We believe we need to build a strong relationship model that simply does not exist for most of us right now. So how do we actually DO that? How do we better understand and communicate with each other? We have identified some actionable strategies that have worked for us and our panel, and here we synthesize them for you, the reader who would like to implement these strategies.

#### *19.4.1 Provide Funding Sources*

We heard time and again from our panel that one way that applied privacy researchers learned about academics' work is through participating in industryfunding programs (e.g., serving as a reviewer for proposals to a private sector research grant). Relevant research may be discovered this way, and to further academic research so that we can all make more progress, industry should help fund it. This helps academics accelerate their work, which also helps industry get to results quickly. For instance, rooted in the advocacy of internal researchers, Facebook launched a new research funding proposal to fuel academic research on inclusive privacy (see https://research.fb.com/programs/research-awards/ proposals/peoples-expectations-and-experiences-with-digital-privacy-request-forproposals/). As a part of this funding, awarded academic researchers provide updates to Facebook research partners throughout the progression of the research so that those working on related, applied questions at Facebook can benefit quickly from the latest results. Additionally, the academic researchers can learn from how their industry counterparts are considering using the research to shape product development in order to make their work and its implications more valuable to the applied sector upon publication. Touchpoints should be created during academicindustry partnerships so that applied researchers and academics can learn from one another.

Likewise, Mozilla has funded privacy research grants over the last several years with diversity and transparency at the heart of this program. Privacy research that resulted from this funding led to direct impact on Mozilla's products such as changes to what Mozilla displays when one uses private browsing mode. In addition to funding research practices, funding academics in nontraditional ways can also greatly contribute to productivity and mutual benefit, such as providing funds for childcare over the course of the grant.

Sure there are existing grants, but are they good enough? A true program/collaboration would involve long-term relationships, like conferences or "labs" that connect academia and industry, and they would involve ongoing commitments, not just a project in a single point in time. Current grants are often perceived as pertaining to a specific topic and project goal. We need to foster relationships and open the time and space for ongoing relevant questions. Companies can also give money to foundations or other organizations who can then determine and fund research based on their own values. This type of intermediary can be important to be sure the results are not influenced or perceived to be influenced by the industry group who is funding the work.

#### *19.4.2 Invest in the Next Generation of Scholars*

Applied researchers can align their efforts with privacy academics' overarching goals by investing in the next generation of privacy scholars. Because academic researchers spend a great deal of time mentoring and teaching students, focusing joint energy on bolstering student skills and experiences will foster better collaborations with applied researchers, not just now, but into the future. For example, many large technology companies fund capstone course projects for graduate and undergraduate students studying computer science. Students receive a real-world problem that applied researchers are currently facing and are provided resources to study related concepts and report back with proposed solutions at the end of the semester. Importantly, these courses allow for cross-academia and applied sector collaboration during which both applied researchers and professors provide guidance to students.

Applied researchers should be actively involved in the training of future privacy researchers in academia, regardless of whether students end up going into applied or academic careers. Providing students with context about applied problems, solutions, and careers will ultimately strengthen their research and impact across privacy science as a whole. For example, both Facebook and Google invest in conducting "Research Jams" and other collaborative hands-on workshops with industry researchers and students at different universities aimed at developing rigorous applied research plans about privacy topics. These events provide students with exposure to solving applied problems through research as well as to applied research career trajectories.

In my work, I get to partner with professors and students on projects—they do research on their own, and I bring it together for our government customers. I advise on the work, and sometimes collaborate, but there is still separation. The strongest partnerships I've seen between academic and industry partners are through CMU's Human-Computer Interaction Institute's Capstone courses. As both a teacher and an advisor, I've been thrilled to observe student teams successfully collaborating with corporations and nonprofits to improve all types of experiences. In these situations, they are sharing and learning from each other resulting in positive outcomes including employment for graduates.

– Carol Smith, *Carnegie Mellon University*

#### *19.4.3 Sharing Work*

Both academic researchers and applied researchers need to find ways to regularly and relevantly share their work with one another. Academic privacy researchers should consider publishing summaries of their work for mainstream audiences, send relevant papers to applied researchers they have previously connected with, and present at both academic and applied research conferences. We may need more and new mechanisms to do this well. For instance, perhaps we need to expand the open source and open data movements into the privacy sector and create shared repositories of both academic and applied research *by topic*. Although there are challenges to doing so (e.g., private sector confidentiality), there are ways to start to build these bridges. For example, the Facebook Privacy Research Team has partnered with Trust, Transparency, and Control Labs to share privacy research insights with external audiences so all can learn from one another. In addition to these types of forums being useful for sharing results across academia and applied research, applied researchers across different companies and industries, in and of themselves, also benefit from shared knowledge bases.

Members of our panel described times when academic partnerships hadn't worked out, when there were concerns or disagreements in how results would be shared at the end of a project (e.g., one academic wanted to publish based on collaboratively collected data without having the partner applied researcher review the final product). These disagreements can lead and have led to collaborations failing. Thus, it's vital that applied researchers scope collaborative or funded academic research projects with publication as the ultimate goal and that both sides of any collaboration talk openly about and agree to publication guidelines (in writing) at the very beginning of the project.

Lastly, academics can help make their work more applicable in industry settings by ensuring insight-based recommendations are actionable, plausible, and in digestible language for non-scientists. This allows applied researchers to easily translate academic findings and implications into design and engineering "languages" for stakeholders on the ground who might be implementing academics' suggestions.

Provide a '5 things you need to know about privacy' document that accompanies any academic publication. This type of document is not about dumbing down the work; rather, it is a way to translate the work for all to read, like how we should explain concisely and clearly to our users!

– Julie Schiller, *Google*

#### *19.4.4 Sharing Data and Resources*

It would be remiss to not identify one of the ways that privacy academics consistently ask for partnership with applied researchers—they request data to be shared with them for their own use and analysis. On the surface, this may seem like a simple ask. However, the sensitivity of sharing consumers' data from a business or clients' data from a nonprofit generates, ironically given the topic of study, a number of privacy considerations that need to be addressed (e.g., Has consent been obtained? Can data be effectively deidentified? Will the analyses directly benefit consumers/clients?). Sometimes it is easy to answer all these questions and other necessary questions with "yes." For example, Facebook has provided aggregated and de-identified data sets to academics working on research for social good. But sometimes the answers to the privacy questions above are more fuzzy, and in those cases, applied researchers simply cannot justify sharing data.

The best practice here is not to ask these questions post hoc but instead to define how data can be shared at the beginning of a collaboration so the right security and privacy measures can be put in place. This will allow academic and applied researchers to work together to determine what inputs are essential to project goals and then work from there to determine if, when, and how data sharing will be executed.

One technique to include more academic work in applied research is to include a literature review as the foundational part of setting up projects. Julie Schiller, a UX Research Manager at Google, thinks that those who have academic training can bridge by "using the skills we were taught academically to give a thorough overview of existing work for the team to better understand the project and broader problem." Janaina Pantoja, a UX Researcher and Manager at eBay, starts most of her research with literature reviews. She takes advantage of the huge amount of research that has already been conducted on e-commerce, in general, and on eBay, in particular: "If I don't take the time to understand what is in the literature, I may not be able to synthesize and analyze my data properly. Sometimes it is about language gap we study topics that others might have studied outside of industry, and there are concepts and terms that have been formalized elsewhere *...* sometimes you are saying the same thing that was said in academia already."

One might think that adding literature reviews to the process is time-consuming and inefficient. What many applied researchers may not know is that it actually does not take that much extra time to conduct the literature reviews that Julie and Janaina find so valuable. Many articles are publically available on Google Scholar, ResearchGate, and at local libraries, and conducting a literature review upfront can save you time later.

Collaborating with academics for literature reviews is an excellent way to bridge the gap. Academics can recommend the articles to read to get up to speed quickly on a topic. In fact, putting out a little bit of funding for various literature reviews might be a good way to incentivize academics (likely students) to do this. New students are always looking for new topic areas, and this is a mini step toward shared knowledge and better partnerships without having to have done a lot of research in the area already and without having to commit to the area just yet.

This is why they hire us—to know the rigor and then to make those short cuts, in ways that do not detract from the proper study.

– Janice Tsai, *Google*

Similarly, another way to bridge is to have academics join industry. They can bring the rigor that is second nature to them to industry and teach industry folks to slow down just a bit and learn from what already exists in academia. We have seen this type of cross-over to be very successful at organizations like Facebook and Google, who often hire academics or sponsor them for a period of time (e.g., a sabbatical). Employing academics and grad students as interns and student researchers to move projects/programs forward is also a way to bridge.

The editors of this book have been working on bridging academia and industry for a few years now with a number of initiatives, including:

This book is another step in the direction of getting academics and applied researchers collaborating, sharing, and sitting at the same table, thinking about and working on the same problems together. We believe that it is essential for us to work together to accomplish greater impact through more comprehensive work. What will you do to build a bridge?

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.


**The Use of CRISPR-Cas9 Genome Editing to Determine the Importance of Glycerol Uptake in Wine Yeast During Icewine Fermentation**

#### **Jared Muysson 1, Laurianne Miller 2, Robert Allie 1 and Debra L. Inglis 1,2,3,\***

Received: 11 October 2019; Accepted: 28 October 2019; Published: 30 October 2019

**Abstract:** The high concentration of sugars in Icewine juice causes formidable stress for the fermenting *Saccharomyces cerevisiae,* causing cells to lose water and shrink in size. Yeast can combat this stress by increasing the internal concentration of glycerol by activating the high osmolarity glycerol response to synthesize glycerol and by actively transporting glycerol into the cell from the environment. The H+/glycerol symporter, Stl1p, has been previously characterized as being glucose repressed and inactivated, despite osmotic stress induction. To further investigate the role of Stl1p in Icewine fermentations, we developed a rapid single plasmid CRISPR-Cas9-based genome editing method to construct a strain of the common Icewine yeast, *S. cerevisiae* K1-V1116, that lacks *STL1*. In an Icewine fermentation, the Δ*STL1* strain had reduced fermentation performance, and elevated glycerol and acetic acid production compared to the parent. These results demonstrate that glycerol uptake by Stl1p has a significant role during osmotically challenging Icewine fermentations in K1-V1116 despite potential glucose downregulation.

**Keywords:** Icewine; *Saccharomyces cerevisiae*; hyperosmotic stress; CRISPR-Cas9; glycerol transport; *STL1*

#### **1. Introduction**

Icewine is a signature sweet dessert wine that is fermented from the juice of naturally frozen grapes. Since much of the water in the grape is frozen at harvest, the pressed Icewine juice is highly concentrated in solutes such as sugars, organic acids, and nitrogenous compounds. For Icewine juice produced in the Niagara Peninsula of Ontario, Canada, the average sugar concentration for Vidal Icewine juice was reported at 39.3 Brix for 298 Icewine juice samples (approximately 450 g/L sugar) [1]. The high sugar concentration presents a significant osmotic stress for the fermenting *Saccharomyces cerevisiae*. High osmotic stress, commonly referred to as hyperosmotic stress, causes prolonged fermentation times, less than ideal alcohol yields, reduced biomass growth, and elevated levels of undesired metabolites [2,3]. The undesired metabolites, namely acetate, acetaldehyde, and ethyl acetate, indirectly arise because of the increased synthesis of glycerol, the primary compound responsible for the adaptation to hyperosmotic stress [4]. Synthesis of glycerol is a two-step process that involves the reduction and dephosphorylation of the glycolytic intermediate, dihydroxyacetone phosphate (DHAP). Glycerol synthesis is not redox neutral, as one equivalent of NADH (nicotinamide adenine dinucleotide (NAD+) + hydrogen (H)) is required per synthesized glycerol, resulting in the production of NAD+ [5,6]. Therefore, a second reaction is required to reduce the generated NAD+ back

to NADH. The NADH generating step of glycolysis, the oxidation of glyceraldehyde 3-phosphate by glyceraldehyde 3-phosphate dehydrogenase, is balanced by coupling this step to alcoholic fermentation, where acetaldehyde is reduced to ethanol by alcohol dehydrogenase, regenerating NAD+ and making this a redox neutral process. Excess oxidation of NADH through glycerol synthesis can lead to a significant shift in the NAD+/NADH ratio [7]. Due to the importance of NADH for the reduction of acetaldehyde to ethanol, reduced levels of NADH relative to NAD+ causes insufficient acetaldehyde reduction, thus leading to elevated levels of acetaldehyde, and subsequently acetate, due to acetaldehyde oxidation by aldehyde dehydrogenases [7–9].

In addition to glycerol synthesis, yeast can also actively uptake glycerol from the extracellular environment [10]. One such transporter, Stl1p, is a plasma membrane H+/glycerol symporter. The proton-glycerol symport mechanism of Stl1p allows for extracellular glycerol to be imported against a concentration gradient, at the cost of H+ influx into the cell [11]. As the name *STL* (sugar transporter like) suggests, the protein is one of 34 sugar permeases in yeast and is part of the major facilitator superfamily (MFS) [12]. In laboratory yeast strains of *S. cerevisiae*, *STL1* is reported as upregulated by salt-induced osmotic stress and repressed and inactivated by glucose, but glucose repression was overcome at high temperatures [11,13]. Previous gene expression analysis on the short-term hyperosmotic stress response in wine yeast to grape must inoculation revealed that *STL1* was highly expressed between the first 10 and 30 minutes, before rapid downregulation to near undetectable levels [14]. Another gene expression analysis study found that *STL1* expression upregulation was very low in *S. cerevisiae* wine yeast throughout table wine fermentations and in glucose-containing stress conditions [15]. Conversely, we have previously reported that *STL1* is the most highly upregulated gene in *S. cerevisiae* K1-V1116 during osmotically challenging Icewine fermentations, with a 25-fold increase relative to that measured in table wine fermentation being seen on the fifth day of fermentation [16].

While gene expression analysis is a useful tool for identifying potential gene targets associated with a given phenotype, further investigation is often required to confirm protein function and phenotypic impact. Genetic modification is one such tool that can be used for gene function elucidation. Given the hyperosmotic conditions of Icewine, and the differences between laboratory yeast strains and wine yeast strains, constructing engineered wine yeast strains would be optimal for studying Icewine hyperosmotic stress condition responses [17–19]. While *S. cerevisiae* is amendable with existing genome editing techniques, primarily through the chromosomal integration of selectable markers in haploid auxotrophic strains, applying these techniques to industrially relevant diploid and polyploid strains, like wine yeasts, can be challenging and time-consuming [20,21]. Luckily, with the advent of CRISPR-Cas9 genome editing methods, yeast strain engineering has become rapid, efficient, and multiplexed [22,23]. At the time of this work, there has only been one other published study on the use of CRISPR-Cas9 for genome editing in wine yeasts. The work of Vigentini et al. (2017) used a double plasmid CRISPR-Cas9 method to eliminate *CAN1*, an arginine permease, in the popular wine yeast strains, EC-1118 and AWR1796, thus lowering urea production [24].

In this work, we developed a rapid and effective CRISPR-Cas9 genome editing method for the wine yeast *S. cerevisiae* strain, K1-V1116, which is commonly used for Icewine production. Using a single linearized CRISPR-Cas9 plasmid, that is repaired in vivo with a linear DNA fragment containing the target sequence of the sgRNA, we were able to construct a K1-V1116 strain featuring the complete removal of the *STL1* open reading frame. Subsequent Icewine fermentations were conducted with K1-V1116 and K1-V1116 Δ*STL1* strain. The reduced fermentation performance, along with increased glycerol and acetic acid production per gram of sugar consumed, of the Δ*STL1* strain, provides further evidence that Stl1p contributes to the osmotolerance of K1-V1116 during Icewine fermentations, despite the high concentration of glucose.

#### **2. Materials and Methods**

#### *2.1. Yeast Strains and Media*

The commercial wine yeast used in this work was *S. cerevisiae* K1-V1116 (Lallemand, Montreal, QC, Canada). K1-V1116 strains were propagated using YPD (0.5% (*w*/*v*) Yeast Extract, 1% (*w*/*v*) Peptone, and 2% (*w*/*v*) Dextrose), unless otherwise stated. For solid media, 1% (*w*/*v*) agar was added. For selection media geneticin (G418) (Teknova, Hollister, CA, USA) was supplemented on YPD plates at 200 μg mL−1.

#### *2.2. Description of CRISPR-Cas9 Method*

The CRISPR-Cas9 method used in this work was built upon the work by the Tom Ellis lab [25]. The plasmid used, pWS173, contains cassettes for the yeast expression of both Cas9 and sgRNA (Figure 1). The yeast-optimized Cas9 contains a nuclear localization sequence and is driven by the PGK1 promoter. The optimized sgRNA cassette contains a yeast tRNA and 5' HDV ribozyme and is flanked by 500 bp junk DNA homology arms. In pWS173, the sgRNA cassette contains a BsmBI-flanked GFP dropout for the 20 bp protospacer sequence required for targeted endonuclease activity. The method relies on the repair of a BsmBI linearized pWS173 with a 1.1 kb linear DNA fragment, which has the sgRNA cassette, with a 20 bp protospacer sequence, inserted within. The sgRNA repair DNA is generated using pWS173 as a template, with two PCR amplicons containing primer overhangs that encode for the protospacer sequence and the terminal sequence of the adjacent fragment. The two protospacer encoding amplicons are then assembled using overlap extension PCR, as described in Section 2.3. When the linearized plasmid, sgRNA repair DNA, and donor DNA are transformed into yeast, the homology arms on the sgRNA repair DNA and pWS173 are recognized and the plasmid is circularized (Figure 2a). The circularization of the plasmid restores its functionality, offering G418 resistance and expression of CRISPR-Cas9 and the active sgRNA species (Figure 2b).

**Figure 1.** Representation of pWS173 plasmid (Benchling).

**Figure 2.** Schematic of DNA components which are transformed in yeast (**a**) and in vivo repair of pWS173, and subsequent expression of CRISPR-Cas9, sgRNA, and G418 resistance cassettes (**b**). The CRISPR-Cas9 ribonucleoprotein recognizes the genomic target sequence and cleaves the site, resulting in a double-stranded DNA break in the genomic DNA. Subsequent homology directed repair with the exogenous donor DNA results in successful genome editing (**c**).

#### *2.3. Cloning*

The plasmid used in this work, pWS173, was a gift from Tom Ellis (Addgene plasmid # 90960). Plasmids were propagated in *NEB Stable*™ *Competent Escherichia coli* (New England Biolabs), under the selection of LB (1% (*w*/*v*) Tryptone, 0.5% (*w*/*v*) Yeast Extract, 1% (*w*/*v*) Sodium Chloride) supplemented with 50 μg mL−1 kanamycin (Tocris Biosciences, Bristol, UK). The plasmid was isolated using QIAprep Spin Miniprep (Qiagen, Hilden, Germany), linearized using BsmBI (New England Biolands, Ipswich, MA, USA) and used without gel extraction. The sequences from EC-1118 [26] were used in place of K1-V1116 for all construct and primer design purposes, since the sequence of EC-1118 was available on the Ensembl genome browser. Benchling (San Francisco, CA, USA) was used for plasmid and construct design. The target sequence for CRISPR-Cas9 sgRNA, used in K1-V1116, was designed using the Benchling CRISPR tool, with the default parameters for the NGG PAM single guide RNA for Cas9. The R64-1-1 *S. cerevisiae* reference genome was used for off-target analysis. The target sequence used can be found in Table A1. Donor DNA and sgRNA repair DNA was synthesized using 2-part overlap extension PCR, using Q5 polymerase (New England Biolands, Ipswich, MA, USA) and the primers in Table A2. An initial annealing temperature of 55 ◦C was used for most PCR reactions, with

the temperature adjusted empirically to resolve low yield reactions. An extension time of 15 second per kb and 35 cycles was used for all PCRs. For overlap extension PCR, 0.5 μL of the PCR mix each fragment was used as templates in a 50 μL reaction. Genomic DNA was isolated and purified from K1-V1116 using the Fungi/Yeast Genomic DNA Isolation Kit (Norgen Biotek, Thorold, ON, Canada). PCR amplified DNA was purified using QIAquick Gel Extraction Kit (Qiagen, Hilden, Germany). DNA was quantified using a NanoDrop Lite Spectrophotometer (ThermoFisher, Waltham, MA, USA). PCR amplicons were visualized using 1% agarose-TAE (40 mM Tris, 20 mM acetate, and 1 mM EDTA), containing 0.003% (*v*/*v*) RedSafe nucleic acid staining solution (iNtRON Biotechnology, Seongnam, Korea).

#### *2.4. Strain Construction*

The *S. cerevisiae* strain, K1-V1116, was transformed using a modified electroporation protocol, which was largely based off the protocol described by DiCarlo et al. (2013) [27]. A colony of K1-V1116 was inoculated into 5.5 mL of YPD. The culture was then incubated overnight at 30 ◦C in a roller drum. The following day, the culture was centrifuged at 8000 G for 1 min in a microcentrifuge. The media was removed, and the pellets were resuspended in 250 μL of room-temperature water and consolidated into a single tube. The cells were washed twice more, using 1 mL of water, and then resuspended in 1.5 mL of 500 mM lithium acetate with 5 mM of dithiothreitol (DTT) before being incubated for 30 min at 30 ◦C in a roller drum. Following conditioning, the cells were collected by centrifugation and washed twice with 1.5 mL of ice-cold electroporation buffer (1 M sorbitol and 1 mM calcium chloride). The final cell pellet was then resuspended in 950 μL of ice-cold electroporation buffer and kept on ice. For each transformation, 100 μL of cells were mixed with linearized pWS173 plasmid, sgRNA repair DNA, and donor DNA. The mixture was then transferred to an ice cold 0.2 cm gap electroporation cuvette and electroporated at 2.5 kV, 25 μF, and 200 Ω. The electroporated cells were recovered in 900 μL of room-temperature recovery media (0.5 M sorbitol and 0.5x YPD) and incubated for 4 h at 30 ◦C in a roller drum. After recovery, 50 μL of culture was plated onto YPD, supplemented with 200 μg mL−1 G418 (geneticin) and incubated at 30 ◦C for 24 to 48 h. The remaining recovered culture was saved at 4 ◦C and plated again if required. Integration events were confirmed with colony PCR using Q5 polymerase (New England Biolabs, Ipswich, MA, USA). To obtain colony PCR DNA, a single colony was mixed into 25 μL of 20 mM sodium hydroxide and boiled at 90 ◦C for 10 min. PCR products were visualized using agarose gel electrophoresis, as described in Section 2.2.

#### *2.5. Icewine Juice*

Vidal Icewine juice was previously acquired from Huebel Grape Estates in Niagara-on-the-Lake, Ontario, Canada and stored at −35 ◦C until use. The Icewine juice was thawed at 7 ◦C for 24 h, before being racked off and filtered through a series of coarse, medium, and fine pore size pad filters using the Bueno Vino Mini Jet filter (Vineco, St. Catharines, ON, Canada). The juice was then filter sterilized using 0.45 μm membrane cartridge (Millipore, Etobicoke, ON, Canada). The final sterile filtered Icewine juice (40.2 Brix) was used for Icewine fermentations. The sterile filtered Icewine juice contained 447 g L−1 sugar, pH 3.79, titratable acidity of 7.0 g L−1 tartaric acid, glycerol at 8.67 g L−1, acetic acid at 0.06 g L−1 and yeast assimilable nitrogen (YAN) at 441 mg N L−1.

#### *2.6. Permissive Condition Growth Kinetics Characterization*

Permissive condition growth kinetics characterization was conducted aerobically in YPD liquid media at 30 ◦C in an orbital shaker. In a sterile bottle with a stir bar, 250 mL of YPD was added, along with 450 mg of yeast cells from an agar plate, for each strain. This culture was stirred for 5 min on a stir plate and then was sampled into 75 mL aliquots in 250 mL Erlenmeyer flasks with sponge stoppers. The flasks were then placed in an orbital incubator set to 120 RPM and 30◦ C, and were sampled every two hours. Optical density at 600 nm (OD600) values were obtained using a Genesys 10S UV-Vis Spectrometer (Thermo Scientific, Waltham, MA, USA) set to 600 nm in absorbance mode, with methacrylate cuvettes. Dilutions were made with sterile YPD as needed, to stay under a reading of 1.0. All readings were done in triplicate.

#### *2.7. Icewine Fermentations and Samplings*

The K1-V1116 strains used for fermentations were prepared as starter cultures. The starter culture consisted of filter sterilized Icewine juice diluted to 10 Brix, with an additional 2 g L−1 of diammonium phosphate (DAP). From YPD plates, four freshly cultured yeast colonies were collected and used to inoculate 100 mL of starter culture media. The culture was incubated until the cell concentration reached 2 × 108 cells mL−1, or approximately 17 h, using a shaker table incubator set to 30 ◦C.

The starter culture was used to inoculate 1 L of Icewine juice to a final cell density of approximately 1 × 107 cells mL−1. The fermentations were incubated at 17 ◦C for 34 days. Samples were taken every day between days 0 and 6, every other day between days 8 and 16, and every three days between days 19 and 34. Before sampling, the fermentations were stirred for 5 min to ensure homogeneity, after which 2 mL of the culture was taken. After centrifugation, the supernatant of the sample was transferred to a clean tube and stored at −35 ◦C for later metabolite analysis. The remaining cell pellet was used for total and viable cell counts using methylene blue on a hemocytometer. Larger 50 mL samples were taken from the initial juice and final wine and stored at −35 ◦C for later analysis. Colony PCR was conducted on the strains at the beginning and end of fermentation to verify the genotype. The fermentations were conducted in triplicate for each strain.

#### *2.8. Metabolite Analysis*

Soluble solids were determined by ABBE bench top refractometer (model 10450; American Optical, Buffalo, NY, USA). Acidity was determined by pH measurement using a sympHony pH meter (model B10P; VWR, Mississauga, ON, Canada), and titratable acidity by titration against 0.1 mol L−1 NaOH, to an endpoint of pH 8.2 [28]. Glucose, fructose, glycerol, acetic acid, amino nitrogen, and ammonia nitrogen were measured using Megazyme assay kits (K-FRUGL, K-ACET, K-GCROL, K-PANOPA, K-AMIAR; Megazyme International Ireland, Ltd., Bray, Co. Wicklow, Ireland). Ethanol was determined by gas chromatography (Model 6890; Agilent Technologies Inc., Palo Alto, CA, USA) equipped with a flame ionization detector (FID), split/split-less injector, and Chemstation software (version E.02.00.493). Separations were carried out with a DB®-WAX (30 m, 0.25 mm, 0.25 μm) GC column (122-7032 model; Agilent Technologies, Santa Clara, CA, USA) with helium as the carrier gas, at a flow rate of 1.5 mL min−1. Metabolite production during fermentation was calculated by the difference in the respective metabolite concentration measured between the time zero point (immediately after inoculation) and at each sampling time point throughout fermentations. Normalized metabolite production was determined by dividing the final metabolite production by the final sugar consumed.

#### *2.9. Statistical Analysis*

XLSTAT-Pro by Addinsoft (New York, NY, USA) was used for statistical analysis. Analysis of variance (ANOVA) with mean separation by Tukey's Honest Significant Difference (*p* < 0.05) and Student's *t*-Test (*p* < 0.05, *p* < 0.01, *p* < 0.001) were used to evaluate differences between variables.

#### **3. Results**

#### *3.1. Development of a CRISPR-Cas9 Genome Editing Protocol for K1-V1116*

#### 3.1.1. K1-V1116 Drug Sensitivity Testing

To assess the selection potential of Geneticin (G418) for the isolation of successful transformants, the wild type resistance of K1-V1116 needed to be determined. Given the proven effectiveness of G418 in previous studies [23,27], a rapid approach was used to determine wild type sensitivity: 200 μL of stationary phase K1-V1116 cultured in YPD was plated onto a series of YPD plates with G418 (140, 280, 560, 710, and 850 μg mL−1) and incubated at 32 ◦C. After three days, only one colony appeared on the lowest concentration plate. As such, YPD with 200 μg mL−1 G418 was selected as the future selection media.

#### 3.1.2. Initial K1-V1116 Transformations

The following step was to assess the G418 positive selection functionality of the intact and linearized pWS173, as well as validate the electroporation protocol developed. As the goal of this work was to develop a rapid and facile genome editing method, testing the use of stationary phase cultures as the source of cells for electroporation was desired. The use of stationary phase cultures would eliminate the need to monitor the OD600, allow for more schedule flexibility and reduce the hands-on time by over 5 h on the day of transformation. While convenient, the previously reported cost of using stationary phase yeast is reduced transformation efficiency, thus we needed to determine if using stationary phase culture would be a viable strategy for future strain construction [29,30]. Three YPD cultures of K1-V1116 were incubated at 32 ◦C in a roller drum until log phase (OD600 of 1.8), 18 h of growth, and 40 h of growth, until prepared and transformed with either 300 ng of non-linearized pWS173 or 300 ng of linearized pWS173 plus 1 μg of sgRNA repair DNA, which would produce a truncated, non-functional sgRNA (Figure 3).

**Figure 3.** Log transformation efficiency of log phase culture, 18-hour culture, and 40-hour culture, with 300 ng of pWS173 or 300 ng of linearized pWS173, with 1 μg of non-functional sgRNA repair DNA. Single transformations were plated in triplicate on YPD with 200 μg mL−1 G418. Transformation efficiency represents the average ± standard deviation.

There was a difference in transformation efficiency between the log phase culture, 18-hour culture, and 40-hour culture. For the repaired pWS173 plasmid, the transformation efficiency of the log phase culture was 14x greater than the 18-hour culture, and 541x greater than the 40-culture. Despite this difference, the 18-hour culture still achieved a transformation efficiency of 1.6 × 105 ± 5.1 × 103 CFUs per μg of DNA. The same trend of transformation efficiency between cell culture phases was shown using the intact pWS173. The transformation efficiency measured at each growth phase of the repaired pWS173 appears greater than that observed with the intact pWS173, although we cannot say if this difference is significant due to the experimental design. The transformation efficiency seen in the 18-hour culture was deemed acceptable for future strain construction given the isogenic goal of this work.

#### 3.1.3. Construction of a K1-V1116 ΔSTL1 Strain

Upon validation of the electroporation protocol and the G418 positive selection of the pWS173, K1-V1116 Δ*STL1* was constructed. For this, the sgRNA repair DNA that contained a target sequence within the *STL1* open reading frame was synthesized (Figure 4a). The donor DNA for homology-directed repair contained 500 bp homology arms, and would result in the deletion of the entire *STL1* open reading frame (Figure 4b). K1-V1116 was transformed with 525 ng of linearized pWS173, 1 μg of sgRNA repair DNA, containing a target in *STL1*, and 1.3 μg of donor DNA. The transformation efficiency was 932 ± 62 CFUs per μg of DNA. Of four selected transformants, all four had the genotype which corresponded with the intended integration event (Figure 4c).

**Figure 4.** Schematic of *STL1* and mechanism of CRISPR-Cas9 genome edit. CRISPR-Cas9 cleaves at the depicted location, resulting in homology directed repair with the Δ*STL1* donor DNA (**a**). PCR genotype scheme used to validate the integration (**b**). Gel image of *STL1* genotype of K1-V1116 in lane 2 and K1-V1116 Δ*STL1* transformants in lanes 3 to 6 (**c**).

Further testing of an additional 15 transformants revealed that one isolate was a Δ*STL1* heterozygote with an additional two transformants maintaining a wild type genotype, thus resulting in an overall homozygous editing efficiency of 84%. Eight transformants were streaked to single colonies on YPD with G418, followed by two more rounds on YPD, in order to eliminate pWS173. The final K1-V1116 Δ*STL1* strain used for further testing was negative for pWS173.

#### *3.2.* Δ*STL1 Growth Characterization in Permissive Conditions and Icewine Fermentations*

#### 3.2.1. ΔSTL1 Growth Kinetics in Permissive Conditions

To ensure that the modification to the Δ*STL1* strain did not cause any unintended permissive growth defects, its growth kinetics were characterized in permissive conditions. As *STL1* is reported as having remarkably low expression during conditions containing glucose, especially in absence of significant osmotic stress, this strain was expected to have a similar growth kinetic profile to the parent strain in YPD [11]. The YPD OD600 growth curve of K1-V1116 and K1-V1116 Δ*STL1* did reveal similar growth kinetics between the two strains (Figure 5). Interestingly, K1-V1116 Δ*STL1* consistently reached a slightly higher stationary phase OD600 value compared to K1-V1116.

**Figure 5.** Growth kinetics of K1-V1116 Δ*STL1* (-) and K1-V1116 (-) cultured aerobically in YPD liquid media at 30 ◦C. Cultures were setup and measured in triplicate. Samples were taken every 2 h, with each point representing a corrected OD600 average ± standard deviation.

#### 3.2.2. ΔSTL1 Icewine Fermentation

The fermentation performance of the K1-V1116 Δ*STL1* compared to K1-V1116 was compared in Icewine juice. Within the first 10 days of the fermentation, K1-V1116 Δ*STL1* began to lag in sugar consumption compared to K1-V1116 (Figure 6). The sluggish performance continued for the remainder of the fermentation, with sugar consumption continuing even as K1-V1116 reached a plateau at around day 25.

The total cell concentrations of strains on day 7 revealed that K1-V1116 Δ*STL1* had only reached around 3.5 × 107 cells mL−1, versus the 5.5 × 107 cells mL−1 of K1-V1116 (Figure 7a). The reduction in viable cells seen in K1-V1116 later in the fermentation was not as prominent with K1-V1116 Δ*STL1* (Figure 7b).

The average total amount of glycerol and acetic acid produced throughout fermentation by K1-V1116 Δ*STL1* is lower than K1-V1116 but K1-V1116 Δ*STL1* produces more glycerol and acetic acid per gram of sugar consumed (Figure 8). The differences in glycerol production appear in the first few days of fermentation, while the differences in acetic acid production appear after day 5.

**Figure 6.** Reducing sugar consumed during fermentation. Fermentations of K1-V1116 Δ*STL1* (-) and K1-V1116 (-) were performed in triplicate, with samples from each trial being analyzed in duplicate. Sugar values represent the average ± standard deviation.

**Figure 7.** Fermentation total cell concentration (**a**) and viable cell concentration (**b**) of K1-V1116 Δ*STL1* (-) and K1-V1116 (-). Fermentations were performed in triplicate, with counts being determined in duplicate. Cell concentrations represent the average ± standard deviation.

The final wine parameters (Table 1) demonstrate the significant differences between K1-V1116 and K1-V1116 Δ*STL1*. As a result of the difference in reducing sugar consumption, K1-V1116 Icewine had 80% more alcohol than that of K1-V1116 Δ*STL1*. The differences in total glycerol and acetic acid were not statistically significant, despite the differences in the final alcohol and reducing sugar levels.

**Table 1.** Icewine juice and final wine chemical composition. Data represent the mean value ± standard deviation from triplicate fermentations, with each sample tested in duplicate. Lowercase letters within the same parameter indicate differences between treatments as tested by ANOVA, with mean separation by Tukey's Honest Significant Difference (HSD0.05).

The final differences in normalized glycerol and acetic acid production were significant, despite the insignificant differences in the total glycerol and acetic acid levels (Table 2). K1-V1116 Δ*STL1* produced nearly 49% more glycerol and 71% more acetic acid per gram of sugar consumed.

**Figure 8.** Average glycerol produced (**a**) and average acetic acid produced (**b**) over the time course of the fermentation. Average glycerol produced (**c**) and average acetic acid produced (**d**) versus sugar consumed over the course of the fermentation. Includes K1-V1116 Δ*STL1* (-) and K1-V1116 (-). Values are the averages of triplicate fermentations, with each sample tested in duplicate for metabolite analysis.

**Table 2.** Consumption of juice components and production of yeast metabolites.

Production refers to the difference between the initial Icewine juice and the final wine of each strain. Data represent the mean value ± standard deviation of duplicate measurements per sample (three winemaking replicates per treatment). Asterisks (\* *p* < 0.05, \*\* *p* < 0.01) indicate significant differences between yeast strains (Student's *t*-Test).

#### **4. Discussion**

The CRISPR-Cas9 genome editing method developed was effective in modifying the commercial wine yeast strain, K1-V1116, with various protocol optimization strategies. The use of a single plasmid which contains all required elements, except for the protospacer sequence, in conjunction with overlap extension PCR generated sgRNA repair DNA, allows for rapid testing of other loci. While the use of primers and in vitro DNA assembly can lead to mutations, the resulting 1.1 kb sgRNA repair DNA can be rapidly sequenced with Sanger sequencing. Alternatively, commonly used sgRNA repair DNA fragments could be cloned into an *E. coli* vector for sequence verification and maintenance. The use of overlap extension PCR to generate the Δ*STL1* donor DNA was fitting for an edit which is not sensitive to single-nucleotide polymorphisms and indels, but such a cloning method is not appropriate for a sequence sensitive edit, like a gene overexpression cassette. Future work that ventures into sensitive edit types will need to rely on other high-fidelity and verifiable cloning methods.

The initial antibiotic resistance testing of K1-V1116 demonstrated that the strain is no more resistant than previously used strains. We believe the rapid test was sufficient, as notable background growth was not encountered for the remaining transformations. The initial transformation trials, with various K1-V1116 cultures as the source for electrocompetent cells, reinforced the previous findings that use of stationary phase cells was many orders of magnitude less efficient than log phase cells [29,30]. For the isogenic goal of this work, the use of stationary phase yeast for electroporation resulted in acceptable transformation efficiencies and greatly reduced the work required for transformation. Whether future work utilizes stationary phase cells will depend on the goal of the project. Conveniently, the use of the repaired pWS173 and sgRNA repair DNA appears to be more effective than a larger circularized plasmid. This observation indicates that the homologous recombination in K1-V1116 is active and potentially comparable to that of many laboratory *S. cerevisiae* strains [31]. The editing efficiency seen with the Δ*STL1* edit, 84%, was high enough for practical uses, and is within the ranges seen across many CRISPR-Cas9 methods [22].

The Icewine fermentation trials conducted with K1-V1116 Δ*STL1* demonstrated the serious consequences that removing the Stl1p transporter has on fermentation performance. The sluggish fermentation, as indicated by the drastically reduced sugar consumption and total cell count, indicates that cell growth was inhibited by the deletion. The higher level of normalized glycerol production suggests that, since glycerol uptake is disrupted, the cell has an increased reliance on endogenous glycerol synthesis to satisfy demand under the hyperosmotic conditions. With the increased glycerol production per sugar consumed, the normalized acetic acid levels increased as well. Given the current understanding of glycerol synthesis and the NAD+/NADH ratio during Icewine fermentations, the increased glycerol demand correlates with higher acetic acid levels [4,8,16]. The exact cause of the reduced fermentation performance still needs to be determined. The increased demand on glycerol synthesis may further increase the NAD+/NADH ratio and disrupt the metabolic flux of the many reactions which rely on NAD+/NADH [32,33]. The inability to meet the glycerol demand could result in cell shrinkage, thus leading to the crowding of macromolecules and the slowing of cellular processes [34]. The accumulation of the toxic fermentation intermediate, acetaldehyde, may also contribute to the sluggish fermentation [35]. The exact cause of this phenomenon appears to not be acutely toxic to the cells, as the viable cell concentration of K1-V1116 Δ*STL1* was relatively stable throughout stationary phase. The maintained viability, despite the reduced performance, is not abnormal for other known causes of sluggish fermentations [36].

These results, in conjunction with previous findings that *STL1* was the most highly upregulated gene during Icewine fermentations, strongly suggest that Stl1p has a role in maintaining internal glycerol levels during the hyperosmotic stress conditions of an Icewine fermentation [4,16]. The previous studies on *STL1* expression reveal that expression is low when glucose is present, even in the relatively stressful environments of table wine fermentations [11,13–15]. Our findings are similar to recent gene expression analysis research in sugarcane bioethanol strains, which showed that *STL1* glucose repression was overcome in 30 and 35 ◦Brix conditions [37]. Future work should investigate whether overcoming glucose repression and inactivation at high sugar concentrations is a shared attribute between strains, or if certain strains have evolved the ability to overcome it. Considering many other yeast species rely on glycerol uptake to satisfy osmotic stress glycerol demand, it appears natural for *S. cerevisiae* strains like K1-V1116 to rely more on glycerol uptake during hyperosmotic stress conditions [38,39]. Furthermore, the role of initial glycerol levels in Icewine juice needs to be determined, as reduced starting glycerol levels may negatively impact the effectiveness of Stl1p-mediated osmotolerance.

#### **5. Conclusions**

Our work demonstrates that CRISPR-Cas9 genome editing is an effective tool for studying the role of glycerol uptake by Stl1p in the commercial wine yeast, K1-V1116. The CRISPR-Cas9 genome editing method developed allows for rapid strain engineering method by employing time- and cost-saving strategies typically only used with laboratory yeast strains. We found that K1-V1116 is not challenging to engineer despite the lack of genome sequence information and oenological nature. The use of the engineered K1-V1116-lacking *STL1* in Icewine fermentations generated significant data, which support the idea that Stl1p contributes to osmotolerance during the harsh hyperosmotic conditions.

**Author Contributions:** J.M. and D.L.I. conceived and designed the experiments; J.M. performed the genome editing experiments; R.A. performed the yeast growth curves in YPD, filter sterilized the juice and determined the chemical analysis of the sterile-filtered Icewine juice; L.M. conducted the Icewine fermentations and measured the yeast metabolites and final wine composition; J.M. and D.L.I. contributed to the writing of the manuscript and J.M., D.L.I. and R.A. contributed to the review and editing of the manuscript.

**Funding:** This project was funded by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC<PHONE_NUMBER> and NSERC DDG-2018-00007).

**Acknowledgments:** We would like to thank the Tom Ellis lab for sharing pWS173 and for assembling an in-depth guide on using their CRISPR/Cas9 method. We appreciate the additional technical support provided by Sudarsana Poojari.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A2.** Primers used in this work.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Communication*


**Author details**

Navin Khaneja IIT Bombay, Powai, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*SQUID Magnetometers, Josephson Junctions, Confinement and BCS Theory of Superconductivity DOI: http://dx.doi.org/10.5772/intechopen.83714*


**2. Experimentation of impact of soil contamination on vegetables and fruits**

### **2.1 General principles of experimentation**

It has been shown that when environmental conditions allow heavy metals to infiltrate the soil and, implicitly, groundwater, whole food chain is degraded and there is a direct risk of pollution. The dissemination of metals in food chains place as follows: contaminated soil with toxic metals - plants that absorb metals from the soil in the root, stem, leaves and fruit- humans and animals who eating plants contaminated with dangerous toxic metals and drinking water that can circulate through groundwater and surface water that has metals drains in them.

The danger of contamination of soil and plants with heavy metals depends primarily on the species of the plant and the properties of the soil, and second, the amount and concentration of the metal in the soil. But on the other hand the existence of chemical elements that can change the effect of metals and other substances, can reduce or amplify the absorption or adsorption processes in the soil. The adverse effect depends on the mobility and solubility of heavy metals in the soil. As control measures, there must be optimal conditions for passing metals from the soil solution into stable forms.

In plants, the concentration of metals varies depending on the species of the plant, its period of development, the vegetative organs (roots, stems, leaves, shoots, etc. branches, buds, flowers, fruits). There are plant species that can preferentially

accumulate heavy metals according to their vegetative organs, as well as varying amounts. Therefore, in polluted areas, especially mining areas, it is not recommended, to ingest especially green vegetables, because toxic metals quickly get into the leaves due to foliar absorption.

Therefore, for areas at risk of soil pollution and implicitly of plants, the population must selectively consume the plants that grow on those contaminated soils. The selection of plants for consumption is based on the following criteria:

In this study, the following vegetables and fruits were used in a controlled environmental experimentation:

These vegetables and fruits have been chosen because they are frequently eaten, and grown by the inhabitants of suburban areas of Romania, being essential and rich in micromacronutrients, proteins, antioxidants and vitamins beneficial to the human body.

The selected vegetables also have a high capacity of accumulating metals without the phytotoxicity of the plants being observable by consumers. The vegetables studied had a relatively short life cycle (about 60 days) and developed well in pots, in the greenhouse, where a controlled environment was created.

### **2.2 Actual development of experiments**

The heavy metals studied were copper, lead and zinc introduced into potted soils in four different concentrations for the solution of each metal, namely: 1.5%, 3.0%, 4.5% si 6.0%. The solutions were prepared individually, using distilled water, as a solvent and copper sulfate, lead acetate and zinc sulphate, as reagents. 250 ml solution of each metal (Cu, Pb, Zn) with the concentrations for each metal was placed, at 1 kg of soil.

Soil contamination with metal solutions and their concentrations was carried out by homogenizing them evenly in the soil prior to planting in pots, no additional until harvest.

*Evaluate the Impact of Soil Contamination on Vegetables and Fruits DOI: http://dx.doi.org/10.5772/intechopen.110445*

In pots, carrot seeds, parsley, cucumber and salad seedlings, raspberry bushes and strawberry stems were planted, which were 1 year old at the time of planting.

At the same time, the reference samples were also made, which consisted of planting seeds, seedlings, bushes and stems in fertile, uncontaminated soil.

The basic properties of the soil used in the experiments were: PH 6.2, moisture 15.4%, particle elements <18 mm 85–95%, total phosphorus 0.8%, total potassium 1%, total nitrogen 2%.

The analysis of the metal content for samples from soils and plants was made on ash resulting from the samples. The methods and techniques used were consistent with the recommendations developed by [46, 47] using an atomic absorption spectrophotometer [48]. The method used was the spectrophotometric method (atomic absorption in flame) [48, 49].

### **2.3 Experimental results**

**Table 1** presents the content of metals in soil uncontaminated and in soli contamination with Cu, Pb and Zn separated concentrations.

The experimental data obtained for vegetables are presented in the **Table 2** and for fruits in the **Table 3**. Based on them, the mathematical modeling was done.

Also, aspects with the plants during the experimental research are shown in the **Figure 3**.

### **Table 1.**

*The content of metals in soil function of four concentrations.*

### **Table 2.**

*The experimental data from vegetables.*

### **Table 3.**

*The experimental data from fruits.*

### **3. Development of mathematical models**

### **3.1 Study of the transfer coefficient of plants studied**

The transfer coefficient reflects the heavy metal uptake ability from the soil by the plant as a function of the heavy metal concentration in the soil.

The definition formula is (Eq. (13)):

$$\mathbf{C}\_{t} = \frac{\mathbf{C}\_{fp}}{\mathbf{C}\_{\text{is}}} \tag{13}$$

For vegetables and fruits, the variation of the transfer coefficient (Ct) depending on the initial concentration of heavy metal in the plants (Cfp) and on the initial concentration of heavy metal in the soil (Cis,) is represented in the following tables (**Table 4** for vegetables and **Table 5** for fruits).

*Evaluate the Impact of Soil Contamination on Vegetables and Fruits DOI: http://dx.doi.org/10.5772/intechopen.110445*

**Figure 3.** *Aspects with the plants during the experimental research.*