(668b) Exploiting Models to Assess and Guide Genetic Engineering of Microalgae Strains

Exploiting models to assess and guide genetic
engineering of microalgae strains

Andrea
Bernardi¹, Andrea Meneghesso², Giorgio Perin²,
Tomas Morosinotto²,Fabrizio Bezzo*^1
1CAPE-Lab
(Computer-Aided Process Engineering Laboratory) & PAR-Lab (Padova Algae Research
Laboratory), Department of Industrial Engineering, University of Padova

via Marzolo 9, 35131 Padova, Italy

²PAR-Lab
(Padova Algae Research Laboratory), Department of Biology, University of
Padova, via Ugo Bassi 58B, 35131, Padova, Italy

 

*fabrizio.bezzo@unipd.it

Microalgae
are long been recognized as one of the most promising alternatives for biofuel
production. The main advantages of microalgae with respect to other possible
feedstock are the high potential productivity and the absence of competition
with traditional crops for arable land and clean water. However, this potential
is still theoretical and algae production on large scale is not profitable yet.
Several issues need to be addressed to reach this objective, ranging from algae
cultivation and harvesting as well as products extraction [1]. In order to be
economically and energetically sustainable microalgae cultivation must use the
solar irradiance, which is variable on daily and seasonal basis. One of the
main problems of microalgae cultivation in large scale is the low conversion
efficiency of the solar energy. In fact, there is a big gap between the conversion
efficiency obtained in laboratory and in large scale photobioreactors.

Microalgae
have developed a series of mechanisms to modify their photosynthetic apparatus in
order optimize the light utilization in a natural environment, such as
variation in pigment composition and/or spatial organization of the
photosynthetic apparatus [2]. In particular, two main regulatory mechanisms can
be identified: photoregulation, often referred as Non Photochemical Quenching
(NPQ), and photoacclimation. Photoregulation involves the activation of
dissipative pathways to convert part of the light energy in thermal energy in
order to prevent photo-induced damage (photoinhibition) and acts in a time
scale from minutes to hours. Photoacclimation is a slower process acting in
time scales of days and induced a variation on the pigment content of the
microalgal cells as consequence of a variation in the light conditions. However,
certain
properties of microalgae, which have been positively selected in wild-type
species through evolution, can cause a significantly loss of productivity and
are therefore detrimental to large-scale cultivation as the growing conditions
in industrial plants are very different from the natural ones [3]. In this context, genetic
improvements of microalgal strains can play a major role in the development of
biofuels technology. However, addressing such a complex problem via trial-and-error, as is
done currently, presents many drawbacks. First of all, evaluating the performances of a
mutant is a difficult and time demanding task, as it requires to grow several
microalgae cultures under different light conditions and each experiment may
last several days.

First
principles models could be an effective tool to reduce the experimental effort needed to
select the best mutant among different candidates and they can also provide
guidance for genetic engineering by identifying those modifications having the
largest potential impact on productivity.

In
this work we will show how a model developed by the authors [4] can be
exploited to evaluate the performances of different mutants in fast and
reliable way. The model describes the photoproduction, photoregulation and
photoinhibition processes and has been calibrated against chlorophyll fluorescence
and photosynthesis rate measurements of three strains of Nannochloropsis
gaditana: the wild type, a mutant with reduced NPQ (mutant T3.9) and a
mutant with a reduced antenna size and chlorophyll content (mutant E2). After
the calibration the model has been used to evaluate the mutants performances
under multiple light conditions, such as the photoperiods of different months
in a typical Mediterranean country. The advantage of the proposed approach is that to
calibrate the model are necessary only fast and accurate fluorescence
measurements and some measurements of photosynthesis rate. In fact, as
demonstrated in [4] the information contained in fluorescence experiments is
sufficient to describe the dynamics of photoregulation and photoinhibition.

 

In
Figure 1a are reported the photosynthesis rate measurements for N. gaditana
wild type along with the model predictions for the wild type, the mutant T3.9
and the mutant E2. In Figure 1b are reported the predicted photosynthesis rate
profiles under light conditions typical of July in Padova as obtained from the
PVGIS database.

(a)                                                                                      
(b)

Figure 1: (a) Photosynthesis rate measurements for Nannochloropsis
gaditana wild type along with model simulation results and photosynthesis
rate prediction for mutants T3.9 and E2; (b) predicted photosynthesis rate
profile for July irradiantion profile for N. gaditana wild type, mutant
T 3.9 and mutant E2.

 

We
can observe that, even if the photosynthesis rate measurements of the NPQ-less
mutants are higher than the measurements of the wild type also at high light
intensities, when the culture is exposed to intense light for a prolonged
amount of time, the photoinhibition lead to a dramatic reduction of the
photosynthesis rate. For this reason a mutant where the NPQ activity is absent
may not be a good candidate for an outdoor cultivation in the Mediterranean
area and other type of mutants should be investigated. On the other hand, the
mutant E2 is more promising as the model predictions show an higher
photosynthesis rate both in laboratory measurements conditions and for the July
photoperiod. Moreover, if a dense culture is considered the antenna-less mutant
will lead to a more uniform light distribution, which will increase the gap
between the wild type and the mutant.

 

Future
efforts will be devoted to validate the model predictions for different
photoperiods and to include the light attenuation and mixing effect in the
modelling framework. This will allow us to investigate the mutants performances
under more realistic conditions representing a real photobioreactor conditions.

 

References

[1] Mata, T.; Martins, A.; Caetano, N.
Microalgae for biodiesel production and other applications: a review. Renewable
and Sustainable Energy Rev. 2010, 14, 217-232.

[2] Williams, P. J. l. B.; Laurens, L. M. L.
(2010). Microalgae as biodiesel & biomass feedstocks: Review & analysis
of the biochemistry, energetics & economics. Energy & Environmental
Science, 3, 554-590.

[3] Formighieri, C., Franck, F., and Bassi,
R. (2012) Regulation of the pigment optical density of an algal cell: filling
the gap between photosynthetic productivity in the laboratory and in mass
culture. J.
Biotechnol. 162, 115–23

[4] Bernardi, A.; Nikolaou, A.; Meneghesso,
A.; Morosinotto, T.; Chachuat, B.; Bezzo, F. (2015). High fidelity
modelling of light limited photosynthetic production in microalgae using PAM
fluorescence and optimal experiment design. PlosOne. 2016, 11, e0152387