Our team has worked very hard trying to find solutions to local, growing problems. This is the case of mental health , one of our research focuses. We address, unfortunately, a large group of people who suffer the consequences of mental health-related diseases every day due to various reasons. Besides the well-known effects of the COVID19 pandemic , a recent report by the Intergovernmental Panel on Climate Change (IPCC) concluded that rapid climate change poses a growing threat to mental health and psychosocial well-being, causing disorders ranging from emotional distress to anxiety, depression, pain and suicidal behavior (Mental health and Climate Change: Policy Brief, 2022).
Thus, our main target end users are the general population , with a particular focus on children , youth and elderly, who are most vulnerable under stressful conditions as shown in several studies and our own research via surveys. Our team believes that talking about mental health remains a taboo subject. However, by acting seriously and responsibly, we believe we can raise awareness among those who still do not realize of how serious this problem is, with the final goal to stop it or at least minimize its negative impact on the population.
Our bacteria will be engineered to produce enzymes involved in the synthesis of serotonin starting from the amino acid tryptophan. The origin of this tryptophan connects with the second fundamental pillar of our project, the circular economy. With our proposed engineering circuit, we will transform environmental waste from animal (milk) or plant (broccoli) sources into a compound with a biological activity in humans like serotonin. In a future implementation, this will be carried out in collaboration with companies such as “Quesos La Vasco Navarra Albeniz” (https://www.quesoslavasconavarra.com), specialized in dairy products or Ingredalia, an innovative technology company from Navarra that works in obtaining functional ingredients from by-products of the agri-food industry in their environment. Additionally, technology centers such as the National Center of Food Technology and Security (CNTA, https://www.cnta.es) will be key strategic partners to transform these industrial leftovers into new products with an added value. We have already collaborated with these institutions during our project, as shown in our website.
One step further in the implementation process will be the manufacturing of the final product, serotonin, and its commercial distribution. In this regard, we have established a collaboration with companies such as CINFA (https://www.cinfa.com), a leading company in the pharmaceutical market based in Navarra who could contribute to the industrial production of the serotonin and commercialization, and Nucaps (http://www.nucapsnanotechnology.com), who would be in charge of its purification, analysis and encapsulation.
For a final implementation in the market, there are several Spanish and European legislations that would need to be followed. Among them, we would like to highlight:
General Health Law 14/1986.
Law 25/1990 on medicines.
Law 29/2006 on guarantees and rational use of medicines and health products. -Royal Decree 1345/2007 regulating the procedure for the authorization, registration and conditions of dispensing of industrially manufactured medicines for human use.
Law 10/2013, of 24 July, which transposes into Spanish law Directives 2010/84/EU of the European Parliament and of the Council, of 15 December 2010, on pharmacovigilance, and 2011/62/EU of the European Parliament and of the Council, of 8 June 2011, on the prevention of the entry of falsified medicinal products into the legal supply chain, and amends Law 29/2006, of 26 July, on guarantees and rational use of medicinal products and medical devices.
Royal Legislative Decree 1/2015, of July 24, which approves the revised text of the Law on guarantees and rational use of medicines and health products.
Royal Decree 1345/2007, of October 11, which regulates the procedure for the authorization, registration and dispensing conditions of industrially manufactured medicinal products for human use.
Regulation (EC) No. 726/2004: procedures for the authorization and control of medicinal products for human and veterinary use and establishing a European Medicines Agency.
Law 7/2022, of April 8, on waste and contaminated soils for a circular economy.
Based on our research and discussion with specialists in psychology, we are aware that serotonin cannot be consumed directly since it will not be able to cross the blood-brain barrier. In humans, serotonin is produced in two locations: in the brain it acts as a neurotransmitter and, in the periphery, it can act as a hormone or intracellular signaling molecule. Given that externally provided serotonin cannot act in the brain, one alternative strategy is to promote gut-derived serotonin synthesis by orally given compounds, since the majority of serotonin producing cells in the periphery are in the gastrointestinal tract (El-Merahbi et al., 2015). Thus, in the future implementation of our project, we could explore these alternative pathways for obtaining a real benefit via oral supplementation.
Ours is a project involving very complex stages, from the design of the recombinant-protein-expressing bacteria to the final development of nanoencapsulated serotonin supplement, including the optimization of all the intermediate processes.
Therefore, there is a need and a will to continue with this project by future UPNAvarra_Spain team, as this would require several years of work and funding that is currently out of our reach. There are therefore some lines of work that we leave open as a legacy to future teams from our university or any university in the iGEM community to take our work as a base and build something bigger from it. After all, this is how scientific knowledge advances.
Some of these avenues to pursue are:
The urge to produce serotonin in high quantities has to come by first optimising the production of each of the two enzymes participating inits biosynthesis. Our team has already selected the parts eligible to do this (see Engineering page in our wiki), and it will be the first thing for future teams to do the proper cloning and compare the protein production capacity of each DNA construction for the enzymes.
Protein functionality verification:
There will also be the necessity to confirm that the protein folding process is optimal and is not interfering with serotonin synthesis. This inevitably is an arduous task, but crucial nonetheless with our intention, since, besides the protein being synthesised here is optimised for production in E. coli, the proteins come from human sequences, and the folding processes have differences in each of these.
Once we have a functional protein production vehicle, it will be the time to scale up the process. Starting with smaller volumes, knowledge in bioreactors functioning will be key in this step of the process.
If we want serotonin to enter the organism effectively, there is no point in being able to produce this neurotransmitter if we do not know how to get it to the intestine without damaging it. One possible solution to this problem would be nanoencapsulation in protein capsules.
For this, one of the options that we have evaluated has been the collaboration with Nucaps, a company in our environment specialised in the development of nanocapsules made with natural proteins for use in the nutritional and health industry.
Their technology is perfect for encapsulating products like ours, but because of the time it would take to implement this process, our team has not had the time to carry it out. We would love for future teams to follow this path.
Another possible avenue of work would be to look for the end product not to be tryptophan, and not even enzymes, but the bacteria themselves that would act as probiotics facilitating serotonin synthesis in vivo.
This would require firstly that the bacteria have the capacity to synthesise both enzymes and secondly that they are able to take the tryptophan from the medium, transform it into serotonin and excrete it.
This is undoubtedly the most ambitious pathway insofar as it requires studying the living conditions of the bacteria, the correct folding of the enzymes, their correct functioning as catalysts and the entry and exit of tryptophan and serotonin from the bacteria, respectively.
This pathway itself contains several steps, so a single team may not be able to develop all of them. It would therefore be a great pathway for a university that wants to maintain the same line of work over several editions of iGEM.
Optimization of serotonin production in vitro:
The last option would be the most obvious continuation of our project, and would consist of following the steps for modelling that we have described in order to be able to optimise the transformation process, but without giving up performing the in vitro reaction. This has the advantage that we are able to control the conditions of both the bacterial cultures and the reactions.
In addition to studying the kinetics of the reaction for different enzyme concentrations as we have proposed, another option would be to study other conditions of the reactions such as temperature or pH, to see how a hypothetical departure from the optimal points of the two enzymes reduces the yield of the reaction.
The steps to follow for this last route would be as follows:
What are the equations of the model?
The process of conversion of tryptophan to serotonin involves two reactions in series, each catalyzed by a different enzyme. We can express the process as:
A 🡪 cat1 🡪 B 🡪 cat2 🡪 C
In view of a possible production of serotonin in considerable quantities, it is interesting to know the kinetics of each of these reactions, which will be of the form -RA=kobs*CA^(n) , where -RA is the rate of disappearance of reactant A, which is equal to the rate of formation of product B, kobs is the observed kinetic constant, CA is the concentration of reactant A and n is the order of the reaction.
The equation for the second reaction has the same form.
We now proceed to the kinetic study to find out these parameters.
How do we estimate the parameters of the model?
For each of the reactions we would proceed as follows:
First, we put in our reaction flask our reagent t and take a sample at t=0. Then we add the catalyst to start the reaction and proceed to take samples every few minutes. These samples are analyzed by HPLC to know the concentration of the reactant or product at each instant.
As the stoichiometry of the reaction is 1:1, the concentration of the reactant and the concentration of the product give us the same information, since the rate of disappearance of the former will be equal to the rate of appearance of the latter.
We now plot [A] versus t and obtain a graph similar to the one we see.
We note that after a certain time t the reaction concludes and the concentration of A does not vary, so we take only the first data to obtain CA=CA(t).
From this graph, we fit a polynomial equation like the one we see above that relates the concentration of the reactant to time.
If we derive this equation and evaluate its derivative at each of the time instants, we will obtain the value of the reaction rate at these instants.
We know that the reaction rate can be expressed as -RA=kobs*CA^(n), which taking logarithms gives ln(-RA)=ln(kobs)+n*ln(CA), so if we represent the logarithm of the rate versus that of the concentration, the slope of the line will be the order of the reaction and the ordinate at the origin will be the logarithm of the observed constant.
In these expressions, -RA represents the reaction rate, CA the concentration of the reactant, n is the order of the reaction and kobs is the observed kinetic constant for a given enzyme concentration.
How does the rate vary with enzyme concentration?
Finally, to calculate the dependence of the kinetic constant on enzyme concentration, we use kobs=k1+Ccat*k2, where k1 is the kinetic constant of the uncatalyzed reaction and k2 the catalysis constant.
Thus, we repeat the above experiment for different catalyst concentrations and plot the observed constants versus the respective catalyst concentrations. In the corresponding linear regression, k2 will be the slope of the line and k1 the ordinate at the origin.
Why is a model like this useful?
Performing this study and obtaining the kinetic equation is very useful if we intend to optimize the synthesis process of our product since we will be able to calculate the reaction time and the amount of enzyme necessary to achieve a given yield.