Introduction
Alcoholic fermentation -the capacity of some yeasts to extract energy
from single sugars, generating CO2 and ethanol as
metabolic products even in the presence of oxygen- is an important
physiological adaptation. The process allowed the utilization of the
ecological niche given by modern fruits, an abundant source of food that
emerged in the terrestrial environment in the Cretaceous (Dashko et al.,
2014, Piskur et al., 2006). Although best known by their capacity to
produce and metabolize ethanol (Piskur et al., 2006), the diversity of
substrates metabolized by yeasts is enormous, as they exploit the varied
habitats provided by the interphase between plants and animals
(Paleo-Lopez et al., 2016, Kurtzman et al., 2011). This ecological
success is represented by (at least) 1,500 species of known yeasts,
which can be found on a broad range of substrates including the skins of
fruits, cacti exudates, soils and animals, where they can be either
commensal or pathogenic (James et al., 2006, Kurtzman et al., 2011). The
fermentation lifestyle, however, has the special advantage of producing
a toxic product (alcohol), which displaces other microorganisms and
allows yeasts to dominate the environment. For this reason, it
represents a key innovation that probably boosted the diversification of
fermentative yeasts about 100 millions of years ago (MYA) (Dashko et
al., 2014, Piskur et al., 2006). Thus, rapid sugar and nitrogen
assimilation and subsequently efficient ethanol production, even in the
presence of oxygen at the expense of ATP production, represents a key
feature of fermentative yeasts (“Crabtree positive yeasts”, hereafter,
(Gutierrez et al., 2016).
The domesticated Baker’s yeast (Saccharomyces cerevisiae ) with
its large collection of genetic variants, is normally regarded as the
most important yeast for fermentation (Piskur et al., 2006); but several
other yeast species, such as wild yeasts from temperate rainforests
(S. paradoxus at the Northern hemisphere; S. eubayanus at
the South), can produce alcoholic products with considerable efficiency
(Williams et al., 2015, Libkind et al., 2011). In fact, comparing
ethanol yield (i.e., rate of ethanol production per gram of glucose
consumed; a proxy of fermentative performance) among yeasts species does
not always gives a clear pattern of superiority in competitive fitness
for a given species, as fermentative performance is very variable and
depends on a myriad of factors (Williams et al., 2015, Hagman & Piskur,
2015, Hagman et al., 2014, Hagman et al., 2013). Here, mapping trait
values measured under homogeneous conditions on a calibrated phylogeny
would reveal several interesting patterns of phenotypic variation, for
instance, historical events (see below).
It has been proposed that the origin of the fermentative lifestyle in
yeasts occurred in a few steps involving some genomic rearrangements
that affected the yeast lineage since its origin, about 200 MYA, such as
the loss of mitochondrial electron transport (respiratory complex I),
the horizontal transfer of URA1 gene, and a whole genomic
duplication (Hagman et al., 2013, Paleo-Lopez et al., 2016, Dashko et
al., 2014)(see Fig 1a). The relative importance of these rearrangements
on fermentative capacity of Crabtree positive yeasts has some debate.
Some authors, based on phenotypic comparison of Crabtree positive and
negative yeasts concluded that the onset to fermentative capacity in
Crabtree positive yeasts was attained in these several steps (Hagman &
Piskur, 2015, Hagman et al., 2014, Hagman et al., 2013). However other
authors, based on genomic comparisons sustain that it was abrupt and
marked only by the whole genomic duplication event that occurred about
100 millions of years ago (Marcet-Houben & Gabaldon, 2015, Wolfe &
Shields, 1997).
In order to study the origin of fermentative capacity in a phylogenetic
comparative analysis for yeasts, we took advantage of a phenotypic
compilation where several proxies of fermentative performance were
measured in cultures of several species, including Crabtree positive and
negative ones (Hagman et al., 2013). Phylogenetic comparative analyses
are useful statistical approaches for the analysis of phenotypic
variation, since the phylogeny is used as a template for testing
departures from the assumption of common descendance in lineages. Thus,
conclusions should be taken exclusively for the phylogeny and the set of
traits being measured. In this case, measurements were obtained under
strict homogeneous conditions and after several generations. Then,
phenotypic differences will only reflect lineage-level differentiation,
the hallmark of “common-garden” experiments in ecology and evolution
(Kawecki & Ebert, 2004, Linhart & Grant, 1996). We applied a
particular comparative procedure to those data (the “lasso-OU”
algorithm, see methods), which detects automatically adaptive shifts in
phenotypic values, permitting a “blind” identification of evolutionary
events that have disproportional influence on phenotypic variation.
Specifically, we explored if multiple events or a single event explains
the actual fermentative capacity of yeasts, after mapping these traits
on the phylogeny. We considered four continuous traits representing
performance (i.e., ethanol yield, EthY; Respiratory Quotient, RQ;
glycerol production. Gly; respiratory quotient and growth rate). EthY is
a measure of general fermentative performance as is quantified as the
amount of ethanol produced per unit of glucose consumed, thus being
central for characterizing fermentation efficiency (Hagman et al.,
2013). RQ, on the other hand is important because fermentation does not
need oxygen as the final electron acceptor, and produces just one
CO2 in the first decarboxylation step. Then,
ethanol-forming yeasts have RQ ratios significantly greater than one,
while non-ethanol forming yeasts have an RQ close to, or equal to one
(Hagman & Piskur, 2015). The justification of Gly relies on the fact
that fermentative yeasts produce this metabolite as a response to
hyperosmotic stress, in an alternative pathway of respiration
(Aslankoohi et al., 2015) (see Table 1). If these variables are
informative enough, then a comprehensive phylogenetic analysis should
detect –above the level of reasonable statistical doubt- the positions
where major phenotypic shifts occurred. As a null hypothesis we included
dry mass growth rate, which represents an undifferentiated measure of
growth performance in all lineages. Given that this variable is neutral
for clade differentiation, phylogenetic signal should be non-significant
and the lasso-OU algorithm should not detect any adaptive shift
on it.