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\begin{document}

\title{Systems-level approaches for understanding and engineering of the
oleaginous cell factory \emph{Yarrowia lipolytica}}



\author[1]{Naghmeh Poorinmohammad}%
\author[1]{Eduard Kerkhoven}%
\affil[1]{Chalmers University of Technology}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\selectlanguage{english}
\begin{abstract}
Concerns about climate change and the search for renewable energy
sources together with the goal of attaining sustainable product
manufacturing have boosted the use of microbial platforms to produce
fuels and high-value chemicals. In this regard, \emph{Y. lipolytica} has
been known as a promising yeast with potentials in diverse array of
biotechnological applications such as being a host for different
oleochemicals, organic acid and recombinant protein production. Having a
rapidly increasing number of molecular and genetic tools available,
\emph{Y. lipolytica} has been well studied amongst oleaginous yeasts and
metabolic engineering has been used to explore its potentials. More
recently, with the advancement in systems biotechnology and the
implementation of mathematical modeling and high throughput omics
data-driven approaches, in-depth understanding of cellular mechanisms of
cell factories have been made possible resulting in enhanced rational
strain design. In case of \emph{Y. lipolytica}, these systems-level
studies and the related cutting-edge technologies have recently been
initiated which is expected to result in enabling the biotechnology
sector to rationally engineer \emph{Y. lipolytica}-based cell factories
with favorable production metrics. In this regard, here, we highlight
the current status of systems metabolic engineering research and assess
the potential of this yeast for future cell factory design development.%
\end{abstract}%



\sloppy


\textbf{Abstract}

Concerns about climate change and the search for renewable energy
sources together with the goal of attaining sustainable product
manufacturing have boosted the use of microbial platforms to produce
fuels and high-value chemicals. In this regard, \emph{Y. lipolytica} has
been known as a promising yeast with potentials in diverse array of
biotechnological applications such as being a host for different
oleochemicals, organic acid and recombinant protein production. Having a
rapidly increasing number of molecular and genetic tools
available,\emph{Y. lipolytica} has been well studied amongst oleaginous
yeasts and metabolic engineering has been used to explore its
potentials. More recently, with the advancement in systems biotechnology
and the implementation of mathematical modeling and high throughput
omics data-driven approaches, in-depth understanding of cellular
mechanisms of cell factories have been made possible resulting in
enhanced rational strain design. In case of \emph{Y. lipolytica} , these
systems-level studies and the related cutting-edge technologies have
recently been initiated which is expected to result in enabling the
biotechnology sector to rationally engineer \emph{Y. lipolytica} -based
cell factories with favorable production metrics. In this regard, here,
we highlight the current status of systems metabolic engineering
research and assess the potential of this yeast for future cell factory
design development.

\textbf{Keywords:} lipid accumulation, cell factory, metabolic
engineering, systems biology

\textbf{Introduction}

Limited fossil fuel resources as well as the growing environmental
concerns associated with their extraction necessitate a substitute for
the traditional chemical synthesis of valuable chemicals. As a renewable
and sustainable alternative, microbial biotechnology has been proven
efficient to produce high value chemicals (Timmis et al., 2017).
Accordingly, an array of microorganisms, from bacteria to yeasts, fungi
and microalgae can be utilized as cell factories to produce a wide range
of bioproducts. Among these, \emph{Yarrowia lipolytica} is increasingly
gaining popularity as a biotechnologically relevant cell factory to the
extent that it is already regarded to as the most promising
nonconventional yeast in bioproduction industry (Markham \& Alper,
2018). Most notably, \emph{Y. lipolytica} is a well-known oleaginous
yeast which can be used to produce lipids and fatty acid-derived
bio-based compounds (Blazeck et al., 2014).

Generally, the emerging interest in \emph{Y. lipolytica} among both
industry and academia is further rooted in other advantages such as
having GRAS (generally recognized as safe) status (Groenewald et al.,
2014), availability of various tools for genetic manipulation (Madzak,
2018), tolerance to different industrial conditions such as different pH
values, higher salt concentration or the presence of organic acids, high
capacity to channel nutrients to commodity chemicals via the efficient
flux through the TCA cycle (Bilal et al., 2020), and also high
potentials in biosynthesis and secretion of proteins (Madzak et al.,
2004). Besides being a desirable host for bioproduction, \emph{Y.
lipolytica} is also a model organism for research on the biology and
physiology of dimorphism, hydrophobic substrate utilization, protein
secretion and more importantly, lipid metabolism (Beckerich et al.,
1998; Beopoulos, Chardot, et al., 2009; Dom\selectlanguage{ngerman}ínguez et al., 2000; Fickers
et al., 2005).

Given the broad use of \emph{Y. lipolytica} as an important
microorganism with biological relevance and biotechnological
applications, significant efforts have been made to expand the knowledge
on this yeast. More recently, with the advancements in omics techniques,
a quantitative systems-level insight into cellular organizations has
made possible to make valuable contributions to bioprocessing (Campbell
et al., 2017). Apparently, being able to analyze the entire genome as
well as globally quantifying all cellular components and their
interactions, does not only expand our knowledge on the biology of the
system under study, but also greatly improves the quality of cell
factory design. In general, there are multiple biotechnologically
important metabolites produced by\emph{Y. lipolytica} and the
application of systems metabolic engineering is in its infancy for this
yeast. Therefore, it is beneficial to provide an assessment of the
state-of-the-art, to evaluate which approaches have let to promising
results and where future research should be directed to. Herein, recent
advances in the implementation of systems metabolic engineering in
optimizing \emph{Y. lipolytica} as a cell factory is being reviewed.

\emph{Y.} \textbf{\emph{lipolytica} as a cell factory}

The industrial application of \emph{Y. lipolytica} has over half a
century of history that began with the discovery of its high lipolytic
and proteolytic activity. These features enable the yeast to grow on a
diverse range of lipid or protein rich materials, where many of them can
be considered as cheap substrates. The high capacity of lipase secretion
was leveraged on by British Petroleum from 1950s through the 1970s, to
pioneer the production of single cell protein (SCP) from\emph{n-}
alkanes as substrate (Groenewald et al., 2014). SCP production based on
\emph{Y. lipolytica} is still available as animal feed additive but on a
smaller scale (Ritala et al., 2017).

Starting from 2000s and following the development of molecular and
genetic tools for engineering the yeast, research studies as well as
commercial productions have been oriented towards leveraging the
abilities of the yeast to produce more than 100 different recombinant
proteins, polyalcohols, aromas, emulsifiers and various organic acids
(primarily citric acid) from industrial waste or by-products
(Ledesma-Amaro \& Nicaud, 2016; Markham \& Alper, 2018; Miller \& Alper,
2019). In addition, the oleaginous phenotype of \emph{Y. lipolytica} has
been of interest as it renders the yeast a promising oleaginous cell
factory that can produce oils and fatty acid-derived compounds. Two
products, New Harvest\selectlanguage{english} EPA oil and Verlasso(r) salmon are commercial
examples of this feature and contain omega-3 fatty acids for which there
have been many clinical studies showing a wide range of health benefits
(Xie et al., 2015). Generally, oils and fatty acid-derived compounds
from \emph{Y. lipolytica} can be used as \emph{e.g.} biodiesel as well
as ingredients for food and cosmetic industries (Miller \& Alper, 2019).

Since the lipid biosynthesis is one of the most important features
of\emph{Y. lipolytica} as a cell factory, a brief overview of general
lipid metabolism will be given (also depicted in Figure 1) while the
details can be found elsewhere (Fakas, 2017; Lazar et al., 2018).
Oleaginous strains of \emph{Y. lipolytica} are capable of accumulating
large amounts of lipids in form of a dynamic storage compartment called
lipid body (LB) which mainly accommodates neutral lipids such as
triacylglycerols (TAGs) (Beopoulos, Chardot, et al., 2009). Metabolism
of lipid biosynthesis and accumulation is well studied in \emph{Y.
lipolytica} , comprehensive understanding of which is vital for opting
any further systems metabolic engineering approach to improve the
production. Accumulated lipid can be biosynthesized whether via \emph{de
novo} or \emph{ex novo} routs. \emph{de novo} lipid biosynthesis is
induced by metabolism imbalance upon an essential nutrient limitation in
the growth media. Following by nitrogen starvation and in the excess of
carbon source, intracellular adenosine monophosphate (AMP) is used to
produce ammonium. Since AMP is an allosteric activator for isocitrate
dehydrogenase, its rapid decrease ultimately downregulates the TCA cycle
and in turn, citrate accumulates in mitochondria (Lin \& McAlister-Henn,
2003). Excess citrate will then be cleaved into acetyl-CoA in cytosol
which will be converted to malonyl-CoA used to produce different fatty
acids (FAs) by further elongation and desaturation (Yuzbasheva et al.,
2019). Finally, TAGs are formed using the activated FAs via the Kennedy
pathway in the interface of ER and LBs (Beopoulos, Cescut, et al.,
2009). \emph{ex} \emph{novo} pathway is an alternative for lipid
accumulation based on the ability of \emph{Y. lipolytica} to grow on a
wide range of hydrophobic carbon sources (such as FAs, oils and TAGs)
and with the help of lipases and surfactants (\emph{e.g.} Liposan) which
in turn facilitates the surface-mediated transport of the substrate. If
the substrate is in excess, they will be stored as LBs comprising TAG
and steryl ester (SE). Whether produced \emph{de novo} or \emph{ex novo}
, the accumulated lipids can be utilized as energy source upon
starvation.

Other than elucidating the metabolic or regulatory pathways of a
metabolite from a microbial cell factory, availability of
well-established genome modification techniques is an essential factor
for optimizing microbial cell factories. In addition to wide
availability of basic genetic engineering tools (\emph{i.e.} host
strains and markers, vectors, promoters, terminators and replication
elements) and conventional genetic modification methods, there is a
continuing effort in developing modern gene/genome editing systems
for\emph{Y. lipolytica} which can greatly speed up the application of
this yeast in systems biotechnology (comprehensively reviewed in
(Ganesan et al., 2019)). Briefly, from 2016, several efficient
CRISPR--Cas9 systems for single and multigene editing as well repression
or activation of genes in \emph{Y. lipolytica} are being developed (Gao
et al., 2016; C. Schwartz et al., 2017, 2018; C. M. Schwartz et al.,
2016; Z. Yang et al., 2020). More recently, a dual purpose CRISPR-Cpf1
system has been suggested that is capable of simultaneous gene
disruption and gene regulation in \emph{Y. lipolytica} (Ramesh et al.,
2020). Furthermore, a suite of genetic tools (EasyCloneYALI) has been
generated that facilitates transformation protocols by providing a
series of pre-designed plasmids and oligos (Holkenbrink et al., 2018).
Although there is still significant opportunity to further improve
CRISPR-based systems in \emph{Y. lipolytica} in terms of multi-gene
editing efficiency or the need for a high-throughput screening
technology after genome editing, the existing systems provide a suitable
platform to be coupled with the systems level tools to further promote
the systems metabolic engineering process.

Generally, \emph{Y. lipolytica} is a biotechnologically important yeast
in which the metabolism and the biotechnologically important metabolites
production pathways have been studied. Moreover, the genomes of several
strains have been sequenced, which together with the increasing omics
data production as well as the current availability of plethora of
genetic engineering and genome editing techniques (Holkenbrink et al.,
2018; Madzak, 2018), has offered perspectives for systems metabolic
engineering \emph{Y. lipolytica} as a potential biotechnological
workhorse.

\textbf{Systems metabolic engineering of \emph{Y. lipolytica}}

The functional genomics revolution and increasing ease of omics data
generation and computational analysis aids in understanding the
mechanisms of complex biological systems and the prediction of their
dynamic properties via systems biology. This paradigm of studying a cell
as a whole is being efficiently implemented to provide a holistic view
of the functioning of microbial cell factories with the goal of rational
re-designing or creating new cellular functions in these organisms.

Under the moniker of metabolic engineering, modifications of biochemical
pathways have been used for decades to achieve desired phenotypes such
as overproduction of a metabolite in cell factories, either using random
mutagenesis or other conventional molecular genetic engineering tools.
However, only after the development of the methods and tools in the
discipline of systems biology has it become practical to approach the
metabolic engineering challenges from a systems perspective. The
development of an optimal strain via systems metabolic engineering can
be performed through two major approaches: omics data\selectlanguage{english}-driven (top-down)
and biomolecular model\selectlanguage{english}-driven (bottom-up). While the bottom-up approach
starts with the reconstruction of an accurate mathematical model of a
specific biomolecular subsystem (\emph{i.e} metabolic, gene regulatory
and signaling networks, or combinations thereof) using existing
biological knowledge; the top-down approach begins with extracting
information from relevant omics datasets (\emph{e.g.} metabolomics,
proteomics, transcriptomics, fluxomics). Figure 2 illustrates these
strategies as well as the classic metabolic engineering.

During the past decade, there have been multiple studies on biomolecular
model analysis and omics data generation that aimed to improve the
understanding and production of many valuable metabolites in \emph{Y.
lipolytica} . Genome scale metabolic models (GEMs) are the most commonly
studied biomolecular networks, the popularity of which arises not only
from their innate nature which directly considers the analysis of the
metabolite production phenotype, but also from the availability of
powerful constraint-based modeling methods. As GEMs can provide a global
view of cellular metabolism, they are influential tools in systems
metabolic engineering. Currently, there have been six GEMs published
for\emph{Y. lipolytica} by different research groups, where each model
includes improvements over previously published models. All six GEMs
have been used for evaluating metabolic engineering strategies with a
major focus on over-producing lipids, \emph{e.g.} triacylglycerols
(TAGs) and to a lesser extent for the increased dicarboxylic acids
production (Kav\selectlanguage{polish}šč\selectlanguage{english}ek et al., 2015; Kerkhoven et al., 2016; Loira et al.,
2012; Mishra et al., 2018; Pan \& Hua, 2012; Wei et al., 2017).

Since obtaining high-quality omics data has become progressively easier,
interest in applying data-driven approaches for acquiring systems-level
knowledge on \emph{Y. lipolytica} have increased (Ledesma-Amaro \&
Nicaud, 2016). While the availability of complete genome sequences
reveal its metabolic pathways and functional potential, various
transcriptomics and a few proteomics analyses have provided useful
information about the dynamics of biological processes, such as the
transition from growth phase to the lipid accumulation phase as well as
its regulatory mechanisms (Morin et al., 2011). There have also been a
few metabolomics and lipidomics analyses in \emph{Y. lipolytica} , which
further provide information on the metabolic pathways under study by
incorporating the changes in metabolite concentrations (Zhao et al.,
2015).

As individual omics analyses (e.g. transcriptomics
\emph{or}metabolomics) are not always able to fully elucidate the
behaviors of a cellular system, combining different omics data (e.g.
transcriptomics\emph{and} metabolomics) may result in a more precise
picture of the biological system being studied. Moreover, integration of
various types of omics data following a bottom-up approach can boost the
predictive capability of the model (Gu et al., 2019). Such combinatory
approaches have been applied in a few studies in metabolic engineering
of \emph{Y. lipolytica} .

Generally, examples of systems-level understanding as well as metabolic
engineering efforts in \emph{Y. lipolytica} powered by systems biology
knowledge and tools will be discussed below in relative sections.
Furthermore, Table 1 summarizes the systems-level optimizations of
lipid-derived as well as non-lipid products in \emph{Y. lipolytica.}

\textbf{Lipids and fatty acid-derived products}

Various studies have been performed to gain a systems-level
understanding of lipid metabolism with focus on three main questions:
the main source of NADPH that lipid accumulation demands; gene
regulation that coincides with lipid accumulation; and transcription
factor networks that are underlying the expressional changes during
lipid accumulation as will be detailed below. In many cases, such
studies have not only expanded our knowledge on various aspects of lipid
metabolism, but also yielded proposed targets for genetic engineering
for improved lipid production.

Lipid biosynthesis has a high NADPH demand that could theoretically be
provided through various metabolic pathways. Based on a comparison of
flux distribution obtained by \textsuperscript{13}C metabolic flux
analysis in two strains of \emph{Y. lipolytica} , one of which was
engineered to produce lipids at roughly twice the yield of the wild
strain, Wasylenko \emph{et al.} concluded that the oxidative pentose
phosphate pathway (PPP) is the almost exclusive source of lipogenic
NADPH when \emph{Y.} \emph{lipolytica} is grown on glucose (Wasylenko et
al., 2015). However, when cultured on acetate, another flux distribution
analysis by Liu \emph{et al.} showed the importance of gluconeogenesis
on lipogenic NADPH synthesis (Liu et al., 2016).

To reveal if lipid accumulation is transcriptionally regulated, the
first transcriptomics analysis of \emph{Y. lipolytica} during its
metabolic shift from growth to lipid accumulation indeed showed
significant difference between the expression profile of actively
dividing and lipid accumulating cells (Morin et al., 2011). However, the
genes directly governing fatty acid synthesis were not transcriptionally
regulated, but rather, the authors hypothesized that lipid accumulation
is a passive consequence of the rerouting of carbon fluxes. Upon
nitrogen deprivation a cascade of transcriptional events is triggered
that result in an increased supply of precursor and cofactors necessary
for the synthesis of lipids. Subsequent transcriptomics as well as
multiomics studies confirmed this hypothesis and elucidated the
regulatory role of protein and amino acid metabolism in lipogenesis
rather than the transcriptional regulation of lipid metabolism itself.
By integrating proteome, phosphoproteome and metabolome data,
Pomraning\emph{et al.} suggested posttranslational modification of key
lipogenesis enzymes as the governing regulatory mechanism (Pomraning et
al., 2016). According to later studies, genes related to amino acid
metabolism were appeared to be downregulated upon nitrogen limitation,
while alternative nitrogen supply pathways such as protein turnover and
autophagy genes were upregulated among which an important factor that
affects lipid synthesis was reported to be the metabolism of leucine
(Kerkhoven et al., 2016, 2017). In an independent research where links
between disruption of the \emph{MHY1} gene, involved in dimorphic
transition in \emph{Y. lipoytica} , and lipid accumulation have been
studied, transcriptome data from the \emph{MHY1} mutant strain showed
decreased amino acid biosynthesis while lipid accumulation increased by
13\% based on cell dry weight (G. Wang et al., 2018).

To elucidate if regulatory networks are underlying lipid accumulation,
Tr\selectlanguage{ngerman}ébulle \emph{et al} inferred a genome-wide co-regulatory network using
CoRegNet on the basis of the gene expression data during lipid
accumulation under nitrogen limitation (Trébulle et al., 2017).
According to their reconstructed regulatory network, network modules
representing different transcriptional programs, from growth to lipid
accumulation phases, along with the most influential new transcription
factors (TFs) and co-regulators have been identified. Further, the role
of the top candidates on lipid accumulation were validated
experimentally using a high throughput overexpression system (Leplat et
al., 2018). Based on the identified regulators and target genes, their
findings again support the hypothesis that lipid accumulation is a
consequence of change in carbon fluxes rather than an enhanced lipid
metabolism (Trébulle et al., 2017).

Beyond employing systems-level approaches to gain understanding on lipid
metabolism, it has been used to find systems-level solutions that favor
the oleaginous phenotype in terms of improving its yield, productivity
and titer as well as broadening the substrate range. Nonetheless, most
studies that have improved lipid production can be classified as
classical metabolic engineering, via overexpression of genes that are
directly involved in lipid biosynthesis, deletion of those involved in
lipid degradation, expressing genes required for the consumption of
different carbon sources, balancing cofactors, as well as a combination
of these strategies which are comprehensively discussed elsewhere
(Abdel-Mawgoud et al., 2018; Das et al., 2020; Lazar et al., 2018).
While rationally designed classical metabolic engineering has been able
to push lipid accumulation to laudable levels, systems-level approaches
would be able to also reach the uncharted territories of the lipogenesis
metabolic landscape that would harbor a strain that is robust, versatile
and pushes lipid accumulation to its limits. Systems metabolic
engineering efforts in \emph{Y. lipolytica} have therefore aimed to
approach the theoretical maximal yield via understanding the less
intuitive limiting factors and based on conclusions formulated in omics
and model-based studies.

Accordingly, using a bottom-up approach through the analysis of a
manually reconstructed GEM for \emph{Y.} \emph{lipolytica}(\emph{i}
MK735), Kav\selectlanguage{polish}šč\selectlanguage{english}ek \emph{et al.} suggested a positive effect of oxygen
limitation on lipid production in the productive phase giving the
hypothesis that reduced aeration rate, which is also favored in
industry, might induce lipid accumulation. Moreover, to eliminate
citrate excretion, a reduced glycolytic flux was calculated while
maintaining lipid synthesis rate. Combination of these findings were
used to design a fermentation strategy which resulted in 80\% increase
in the lipid content of biomass and more than fourfold yield improvement
(Kav\selectlanguage{polish}šč\selectlanguage{english}ek et al., 2015). In a later model-based approach, various gene
knockouts and overexpression targets for improved TAG biosynthesis were
predicted by analyzing a comprehensive GEM reconstructed by Wei\emph{et}
\emph{al.} Although not experimentally verified, gene modification
strategies mostly being involved in glycolytic and amino-acid metabolic
pathways were predicted to yield over 55\% increase on TAG production
(Wei et al., 2017). In a most recent model-based study, a modified
version of \emph{i} MK735 was implemented to analyze flux distributions
of \emph{Y. lipolytica} in nitrogen-limited conditions as well as
identify metabolic engineering strategies for improving lipid
production. Since the cellular objective of growing cells during
nitrogen limitation is not obvious, an environmental version of
minimization of metabolic adjustment (eMOMA) was used instead of the
conventional flux balance analysis (FBA) that requires an assumption of
the cellular objective \emph{e.g.} maximized biomass production. This
modeling approach predicted multiple novel and non-intuitive targets for
metabolic engineering while experimental verification of one of the
knockout candidates involved in one-carbon/methionine metabolism
(annotated as methylenetetrahydrofolate dehydrogenase) showed 1.45-fold
increase in lipid accumulation (M. Kim et al., 2019).

Noteworthy is that the current GEM-based analyses did not include any
regulatory information. The integration of further omics data may lead
to a more reliable prediction of gene modification targets \emph{in
vitro} and decrease the false positive predictions. Moreover, top-down
omics-based approaches have not only augmented the biological knowledge
of lipid biosynthesis, but also in some cases non-experimentally
verified suggestions for production improvement have been concluded from
these analyses. Of the non-intuitive examples of these suggestions is
the role of cell wall biosynthesis genes in lipid production, shown by
Pomraning \emph{et al.} where cell wall thickening was observed upon the
exhaustion of extracellular carbon source (Pomraning et al., 2015).
Accordingly, a combination of top-down and bottom-up approaches would
therefore be recommended.

\textbf{3.2. Specific oleochemicals}

While classic and systems metabolic engineering efforts continue to
improve lipid production in \emph{Y. lipolytica} by maximizing flux
through TAG biosynthesis, production of specific valuable oleochemicals
are being studied in this yeast mostly via synthetic pathway
construction.

Many commercially interesting oleochemicals, \emph{e.g.} medium-chain
lipids, are less common in natural resources and are therefore ideal
candidates for systems and synthetic biology-based production due to
their high value (Blazeck et al., 2014). These oleochemicals can be used
as nutraceuticals or pharmaceuticals, such as polyunsaturated fatty
acids (PUFAs) or fatty alcohols (Ledesma-Amaro \& Nicaud, 2016).
Substantial progress has been made towards heterologous production of
such oleochemicals in \emph{Y. lipolytica} , but only recently have
conclusions from systems-level analysis been implemented to improve the
production of non-native oleochemicals. Most of these efforts are
through using the top-down strategy. For instance, in a study by
Dahlin\emph{et al.,} a lipid degradation-deficient strain of \emph{Y.
lipolytica} was used as host organism to express copies of
codon-optimized fatty acyl--CoA reductase from \emph{Marinobacter
algicola.} The final fatty alcohol producer strain was evaluated by
transcriptomics, metabolomics and fluxomics analysis. According to the
results, unlike \emph{S. cerevisiae} , no elicited stress response was
detected in \emph{Y. lipolytica} indicating the non-toxicity of fatty
alcohols in this strain. Moreover, potential limiting factors upstream
of acetyl--CoA, such as glucose uptake, glucose phosphorylation,
conversion to fructose-6-phosphate/fructose-1,6-bisphosphate as well as
pyruvate and citrate were indirectly predicted based on metabolomic
analysis (Dahlin et al., 2019). Transcriptomics of another fatty alcohol
producing \emph{Y. lipolytica} aided to significantly improve the
production upon identifying the upregulation of glycolysis genes in
response to higher fatty alcohol production. This result was leveraged
to optimize the selection of suitable promoters for fatty acyl-CoA
reductase expression, to coordinate the fatty alcohol production with
glycolysis (Zhang et al., 2019). Similar procedure has been performed to
produce pentacyclic triterpenoid oleanolic acid in \emph{Y. lipolytica}
. Based on intracellular metabolome data, a direct precursor of the
triterpenoid was found to be accumulating. Guided by the analysis of
differential transcriptional levels of genes, it transpired that
enhancement of electron transfer by fusion expression of P450 enzyme and
the last step of the biosynthetic pathway could be used to improve
substrate conversion efficiency by 28\% (Li et al., 2020).

In order to repurpose the lipophilic properties in \emph{Y.
lipolytica}for beta-carotene biosynthesis, Wang \emph{et al} . designed
a strategy of global transcription machinery engineering (gTME) to
explore the changes of gene expression levels that benefits carotene
production (M. Wang et al., 2018). This strategy had been introduced for
other complex cellular genotypes in \emph{S. cerevisiae} (El-Rotail et
al., 2017) and\emph{Zymomonas mobilis} (Tan et al., 2016). Accordingly,
a library of randomly mutated versions of the key transcription factor
Yl-Spt15 were expressed in a strain containing a heterologous
beta-carotene biosynthetic pathway. Strains with mutated Yl-Spt15
expression displaying either enhanced or decreased production were
analyzed by transcriptomics, and the most dominant differentially
expressed genes were experimentally tested and those involved in RNA
polymerase, fatty acid metabolism, and specially ketone body metabolism
were shown to be beneficial for beta-Carotene production improvement.
Although the reported beta-carotene yields were far lower compared to
other published yields, their exploited gene targets and pathways can
further guide the rational design of \emph{Y. lipolytica} for tuning
lipophilic production in combination with improved fermentation design.

\textbf{3.3. Non-lipid products}

Although a major focus in \emph{Y. lipolytica} research is on
lipogenesis, this yeast is able to produce other native and non-native
industrially important chemicals such as organic acids (OAs) and
polyols. The most industrially important non-lipid chemicals are OAs
which are direct or indirect intermediates of the TCA cycle. Due to its
oleaginous phenotype, \emph{Y. lipolytica} accommodates a highly active
TCA cycle. However, since biosynthesis of OAs is highly linked to
cellular energy metabolism, reaching efficient production metrics will
require more complex engineering approaches. This renders systems
metabolic engineering profitable tools to optimize OA production.
Nonetheless, also for this group of products has classical metabolic
engineering through rational genetic modifications and optimization of
fermentation condition shown to enhance the productivity (reviewed in
(Finogenova et al., 2005)). Meanwhile, efforts to elucidate the
underlying systems-level production mechanisms are crucial to optimize
cell metabolism favorable for OA production. In terms of citric acid
(CA) production, Sabra \emph{et al.} have focused on transcriptome and
fluxome data in a citrate producing strain of \emph{Y.lipolytica} .
While controlling the oxygen supply on mixed substrates proved
instrumental to improve the citrate production titer more than 3 fold,
multi-omics data analysis have provided pentose phosphate pathway and
glyoxylate cycle genes as targets for metabolic engineering of CA (Sabra
et al., 2017). In another study, after reporting \emph{Y. lipolytica} as
the best CA producer among more than 40 yeast strains\emph{,} Kamzolova
\emph{et al} ., studied the underlying overproduction mechanism using
growth limitation assessment and enzyme activity measurements which
helped in fermentation design for enhanced production (85 g/L)
(Kamzolova \& Morgunov, 2017). A more recent screening also showed that
the production of CA strongly strain dependent, therefore, screening for
overproducers and studying the underlying molecular mechanisms is of
great importance (Carsanba et al., 2019). Noteworthy is that due to the
relations between lipid and CA metabolism, knowledge on the regulation
surrounding lipid accumulation as well as nitrogen limitation can have
considerable relevance for CA production studies.

The production of succinic acid (SA) in \emph{Y. lipolytica} has also
undergone metabolic engineering efforts (Abdel-Mawgoud et al., 2018).
Unlike SA producing bacteria, which are among the most successful
producers to date, \emph{Y.} \emph{lipolytica} can tolerate low pH and
thrives under acidic conditions. This simplifies downstream processes as
the bacterial fermentation broth is rather kept near neutral pH and
requires an additional acidification step for SA purification which
causes byproduct formation (Ahn et al., 2016). Although high titers have
been achieved (160 g/L) in an engineered strain lacking active succinate
dehydrogenase (SDH) to prevent oxidation of succinic acid to fumaric
acid, low yield and productivity hinders the implementation of \emph{Y.
lipolytica} for industrial production. SDH deficient mutants of \emph{Y.
lipolytica} are growth defective in cultures using glucose source
(Yuzbashev et al., 2010). To circumvent this problem Yuzbashev \emph{et
al.} have isolated an improved SA producer from combined induced
mutagenesis and metabolic evolution, which could more efficiently
utilize glucose (Yuzbashev et al., 2016). \textsuperscript{13}C flux
analysis on SDH deficient mutants was performed to investigate the
contribution of various metabolic pathways in SA formation (Yuzbashev et
al., 2016), however, to fully reveal the molecular mechanism by which
adaptive evolutionary approaches have resulted in a beneficial phenotype
would require genome sequence analysis of the evolved and unevolved
strains. A further problem hindering improvement of SA production is the
accumulation of the by-product acetate (Cui et al., 2017). By metabolic
pathway analysis, Cui \emph{et al.} have reported CoA-transferase
activity in mitochondria as the major source of acetic acid. Deletion of
the responsible gene, as well as further overexpression of the key
enzymes of the oxidative TCA resulted in an improved SA titer and no
acetic acid accumulation in defined media (Cui et al., 2017). There have
been successful systems metabolic engineering approaches for SA
production in other microorganisms, such as GEM-based SA production
improvement on \emph{S. cerevisiae} (Agren et al., 2013) as well as
other omics-based studies (Otero et al., 2013). Similar approaches could
be applied to \emph{Y. lipolytica} to yield industrially feasible SA
production levels in this yeast.

\selectlanguage{greek}α-\selectlanguage{english}ketoglutarate (\selectlanguage{greek}α-\selectlanguage{english}KG) is another OA for which \emph{Y. lipolytica}seems
a promising production host. The main strategy being used to improve its
production is still based on fermentation design as well as a few
genetic engineering in the direct biosynthetic enzymes (Guo et al.,
2016). Therefore, while systems-level insight is still desired to
investigate key metabolic processes influenced and regulated by the
known factors that positively affect \selectlanguage{greek}α-\selectlanguage{english}KG production, there are a few
studies based on omics data. In a study to improve \selectlanguage{greek}α-\selectlanguage{english}KG production, Guo
et \emph{al.} , have assessed the roles of specific transporters (Guo et
al., 2015). To this end, they have identified six endogenous putative
transporter genes using a comparative genomics approach and by
expressing them in an \selectlanguage{greek}α-\selectlanguage{english}KG-producing wild-type strain, the production of
this OA has improved while reduced pyruvic acid (a byproduct)
concentration was observed. Their study showed whole genome analysis for
transporters can be helpful in improving the accumulation of specific
organic acids. Another comparative genomics method with the aim of
investigating the regulatory mechanisms controlling \selectlanguage{greek}α-\selectlanguage{english}KG production,
showed the positive role of genes associated with the regulation of
mitochondrial biogenesis and energy metabolism in \selectlanguage{greek}α-\selectlanguage{english}KG production,
whereas they have concluded genes related to transformation between keto
acids and amino acids as reducers of the accumulation of \selectlanguage{greek}α-\selectlanguage{english}KG (Zeng et
al., 2016). As another strategy to accumulate \selectlanguage{greek}α-\selectlanguage{english}KG, is to take advantage
of its thiamine-auxotrophic trait. \emph{Y. lipolytica} is unable to
biosynthesize thiamine which is the co-factor for \selectlanguage{greek}α-\selectlanguage{english}ketoglutarate
dehydrogenase which converts \selectlanguage{greek}α-\selectlanguage{english}KG to succinyl-CoA. Therefore, a culture
medium lacking this vitamin will likely favor the accumulation of \selectlanguage{greek}α-\selectlanguage{english}KG
when carbon source is in excess. However, enhanced accumulation of \selectlanguage{greek}α-\selectlanguage{english}KG
by disruption of genes directly affecting TCA cycle will undoubtedly
cause cellular growth and survival defects circumventing which urges
deeper molecular knowledge. In this regard, similar omics data-based
findings of the study by Walker \emph{et al.} on the effects of thiamine
deficiency on cellular metabolism of thiamine auxotroph \emph{Y.
lipolytica} which was discussed in lipid production improvement
strategies, can be helpful (Walker et al., 2020).

Beyond native OAs, \emph{Y. lipolytica} has been used to produce the
non-native itaconic acid (IA). IA is naturally produced by
several\emph{Aspergillus} species and is a replacement for petroleum
derived products (Bonnarme et al., 1995; Robert \& Friebel, 2016). Due
to shortcomings with the native producer in terms of poor growth and
shear stress sensitivity, its production is studied in alternative hosts
such as \emph{Y. lipolytica} . IA is synthesized by cis-aconitic acid
decarboxylase (CAD) (Kanamasa et al., 2008) and IA production in\emph{Y.
lipolytica} has been established by heterologous expression of CAD
(Blazeck et al., 2015) resulting in IA yields of 0.06 g/(g glucose) at
natural low pH. Although these studies can serve as a promising
stepping-stone toward enhancing IA production in \emph{Y. lipolytica} ,
to establish this yeast as an industrially competitive IA producer,
further efforts are required to improve production metrics. Even while
bottom-up systems-level approaches have been used for IA production in
other microorganisms (Harder et al., 2016), there is still no report on
systems metabolic engineering in \emph{Y. lipolytica} .

Besides OAs, \emph{Y. lipolytica} is a promising producer of sugar
polyols, and there is a currently strong interest in bio-based
production of such value-added functional sugars for use in food and
pharmaceuticals industry as sweetener (\emph{e.g} . mannitol and
erythritol). Recent progress in the use of \emph{Y. lipolytica} to
synthesize these functional sugars, especially the genetic engineering
approaches, are comprehensively reviewed elsewhere (Bilal et al., 2020).
The importance of systems-level studies in this field has been
demonstrated in a study by Yang \emph{et al.} where erythritol
production could be enhanced in an osmophilic strain of \emph{Y.
lipolytica} by cultivation in an osmotic pressure control fed-batch
strategy (L. B. Yang et al., 2014). Proteomics of erythritol production
from glycerol in response to osmotic pressure revealed that highly
ranked differentially-expressed proteins such as enzymes related to
osmotic stress response, the aldo-keto reductases and catalase T had
drastically increased expression levels under hyperosmotic pressure (L.
B. Yang et al., 2015). This information could be leveraged on for future
reverse engineering of \emph{Y. lipolytica} strains for polyols
biosynthesis.

\textbf{Conclusions and perspectives}

For decades, \emph{Y. lipolytica} has been known as a promising
biotechnological chassis not only for its high oleaginous flux, but also
for the production of a variety of native and non-native chemicals.
However, despite advances in genome editing tools which have facilitated
classical metabolic engineering of \emph{Y. lipolytica,} production
metrics for most products still needs improvement. In this regard,
systems biology allows a more in-depth understanding of the intricate
regulation and metabolism which can further help in identifying
non-intuitive strain design targets with the aim of pushing yields to
the theoretical limits. This can in turn expand the industrial potential
of this yeast. Although several \emph{Y.} \emph{lipolytica} strain
engineering studies have begun to involve omics and model-based
approaches, there is significant potential in further use of the
systems-level approach. Accordingly, system-level approaches have been
used for studying lipid-derived products with some promising results,
while non-lipid products have benefited very little from these
approaches. Moreover, both bottom-up and top-down approaches are used,
although not to the same extend in all the products of \emph{Y.
lipolytica} . On the other hand, as the classical metabolic engineering
results generally shows, there is not always a linear correlation
between the overexpression of a gene and its \emph{in vivo} production
improvement which is believed to be caused by various layers of cellular
control. To this end, combination of both the top-down and bottom-up
approaches seems promising. Particularly useful would be metabolic flux
analysis (MFA) which can be performed to measure accurate flux
distributions as model simulations are not always able to give
predictions with enough certainty. Another obstacle hindering
systems-level ME is that many (about 40\% in case of \emph{Y.
lipolytica} W29) \emph{Y. lipolytica} genes are annotated as
hypothetical proteins, which can limit the interpretation of data-driven
information as well as predictive efficacy of genome-based models. This
need can at least partially be addressed using already available
computational approaches such as machine learning that is not only able
to accurately predict translation start sites but also functional
annotations (G. B. Kim et al., 2020). Artificial neural networks,
clustering and other machine-learning algorithms have also shown
promising results in biosynthetic pathway prediction for a target
chemical (Segler et al., 2018) (useful in non-native chemical
productions), prediction of a probability that an enzyme catalyzes a
reaction (Mellor et al., 2016) (useful in GEM reconstruction), and
different gene expression control parameter prediction such as promoter
strength (Jervis et al., 2019) (useful in metabolic engineering).
Finally, \emph{Y. lipolytica} is regarded as a promising host for the
production of various metabolites, therefore, it is expected that
attempts to elucidate systems-level behaviors for one product will also
positively impact on this yeast as a future host for other products.

\textbf{Acknowledgments}

We acknowledge funding from the Novo Nordisk Foundation (grant no.
NNF10CC1016517). Authors declare no conflict of interest.

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\textbf{Figure captions}

\textbf{Figure 1.} Overview of Lipid metabolism in \emph{Y. lipolytica}
.

Solid arrows: chemical conversions and transport reactions, dashed
arrows: multiple chemical conversion steps, dotted lines and arrows:
representing N-limitation consequences. Abbreviations: AMP Adenosine
monophosphate; Cit Citrate; DAG Diacylglycerol; DHAP Dihydroxyacetone
phosphate; F6P Fructose 6-phosphate; FA Fatty acid; FBP Fructose
1,6-bisphosphate; Fum Fumarate; G3P Glycerol 3-phosphate; G6P Glucose
6-phosphate; GA3P Glyceraldehyde 3-phosphate; Icit Isocitrate; IMP
Inosine monophosphate LPA Lysophosphatidic acid; Mal Malate; Mal-CoA
Malonyl coenzyme A; NH4 Ammonium; OAA Oxaloacetate; PA Phosphatidicacid;
Pyr Pyruvate; Suc Succinate; TAG Triacyglycerol.

\textbf{Figure 2.} Schematic procedure of classic \emph{vs.} systems
metabolic engineering and the major identified engineering targets for
lipogenesis in \emph{Y. lipolytica} by each strategy.\selectlanguage{english}
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