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\begin{document}

\title{ATFL1 (Anti-TFL1), a novel orthologue induces flowering in the masting
alpine snow tussock, Chionochloa pallens (Poaceae)}



\author[1]{Samarth *}%
\author[1]{Robyn Lee}%
\author[1]{Dave  Kelly}%
\author[1]{Matthew Turnbull}%
\author[2]{Richard Macknight}%
\author[3]{Anthony Poole}%
\author[1]{Paula Jameson}%
\affil[1]{University of Canterbury}%
\affil[2]{University of Otago}%
\affil[3]{The University of Auckland}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Masting, the synchronous highly variable flowering across years by a
population of perennial plants, has been shown to be precipitated by
many factors including nitrogen levels, drought conditions, spring and
summer temperatures. However, the molecular mechanism leading to the
initiation of flowering in masting plants in particular years remains
largely unknown, despite the potential impact of climate change on
masting phenology. We studied genes controlling flowering in Chionochloa
pallens, a strongly masting perennial grass. We used a range of in situ
and manipulated plants to obtain leaf samples from tillers (shoots)
which subsequently remained vegetative or flowered. Here, we show that a
novel orthologue of TERMINAL FLOWER 1 (TFL1; normally a repressor of
flowering in other species) promotes the induction of flowering in C.
pallens (hence Anti-TFL1), a conclusion supported by structural,
functional and expression analyses. Global transcriptomic analysis
indicated differential expression of CpTPS1, CpGA20ox1, CpREF6 and
CpHDA6, emphasising the role of endogenous cues and epigenetic
regulation in terms of responsiveness of plants to initiate flowering.
Our molecular-based study has provided insights into the cellular
mechanism of flowering in masting plants and will supplement ecological
and statistical models to predict how masting will respond to global
climate change.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Mast seeding (or masting) is synchronous highly variable seed production
among years by a population of perennial plants (Kelly, 1994; Kelly,
Turnbull, Pharis, \& Sarfati, 2008; Schauber et al., 2002). This results
in irregular heavy flowering and seeding events, which occur in a range
of taxa globally, including in various woody and herbaceous endemic
species in the New Zealand flora (Webb \& Kelly, 1993; Schauber et al.,
2002; Kelly et al., 2008). A key question is what external and internal
factors allow the plants to synchronously trigger heavy reproduction in
only some years. Better understanding of those factors, to allow
prediction of changes under global climate change (Kelly et al., 2013;
McKone, Kelly, \& Lee, 1998; Rees, Kelly, \& Bjornstad, 2002) requires
clarification of the underlying genetic mechanisms which control masting
(Samarth, Kelly, Turnbull, \& Jameson, 2020).

Although masting imposes costs, such as missed opportunities for
reproduction, it is selectively favoured in plants which gain benefits
from one or more Economies of Scale (EOS) (Kelly, 1994; Kelly \& Sork,
2002). The two most common EOSs are predator satiation (where seed
predators are not able to consume all the seed production, ensuring
higher survival of the offspring) or more efficient wind pollination
(Kelly \& Sork, 2002). In order for masting to occur, plants need some
synchronising factor, typically a weather cue. Samarth et al. (2020)
have suggested that a likely cue for masting comes from seasonal changes
in summer temperature, so it has been speculated that increases in
global temperatures may alter masting behaviour, although the nature of
this effect is uncertain (Kelly et al., 2013; Monks, Monks, \&
Tanentzap, 2016). Changes in masting would affect the wider community,
potentially impacting on food availability for indigenous seed predators
and the rest of the food chain. Molecular studies, such as Kobayashi et
al. (2013), have the potential to improve mechanistic understanding that
underpins forecasting of changes in mast flowering behaviour. That in
turn can help show how changes in natural conditions may lead to
evolution of flowering-time genes and associated regulatory mechanisms.
However, there is currently very little molecular evidence on the
mechanisms for temperature-driven mast flowering in plants.

Information from model plant species provides useful background to the
special case of mast seeding species. Molecular and genetic approaches
have revealed that various external cues interact with the developmental
processes to regulate the floral transition in perennial plants (Khan,
Ai, \& Zhang, 2014; Kobayashi et al., 2013). Genetic pathways
controlling flowering time in model crops and temperate grasses,
including Arabidopsis (\emph{Arabidopsis thaliana} ), tomato, apple,
rice, barley, wheat and \emph{Brachypodium distachyon} (purple false
brome), show a high degree of conservation between dicot and monocot
species (Shrestha, Gomez-Ariza, Brambilla, \& Fornara, 2014). Both
dicots and monocots share common floral integrator genes including
homologues of florigen, the universal flowering hormone. Florigen, or
FLOWERING LOCUS T (FT) is a 175 amino acid long protein belonging to
the\textbf{p} hosphatidyl \textbf{e} thanolamine \textbf{b}
inding\textbf{p} rotein (PEBP) family, an evolutionarily conserved
protein family found in all taxa of organisms from bacteria to animals
and plants (Karlgren et al., 2011). Phylogenetic analysis of different
homologues of the PEBP gene sequences across the plant kingdom has
revealed three sub-families. These are MOTHER OF FT AND TFL (MFT), FT
and TERMINAL FLOWER 1 (TFL1) (Karlgren et al., 2011). FT and TFL1
protein sequences share 60\% homology with highly conserved amino acid
sequences across diverse species. These genes, however, act
antagonistically to each other: FT promotes flowering whereas TFL1
represses it (Liu, Yang, Wei, \& Wang, 2016).

In the current study, molecular tools were used to investigate the
regulation of flowering of the alpine snow tussock, \emph{Chionochloa
pallens} (Poaceae). This species is one of the most strongly masting
plant species globally (Kelly et al., 2000), which gives the plant
selective benefits through predator satiation (Rees et al., 2002). The
possible impacts of global warming on masting in this species have been
discussed in the literature (Kelly et al., 2013; McKone et al., 1998;
Monks et al., 2016; Rees et al., 2002), but more information on its
flowering mechanisms is required. From various plants, including some
manipulated to induce or prevent flowering, we took leaf samples from
tillers (shoots), some of which subsequently flowered and some remained
vegetative. We later identified each leaf as coming from a plant that
subsequently flowered or one from a plant that remained vegetative
(Appendix S1). We then used ecological transcriptomics (Samarth, Lee,
Song, Macknight, \& Jameson, 2019; Todd, Black, \& Gemmell, 2016) to
identify the potential homologues of PEBP sequences involved in the
onset of flowering. Subsequent structural, functional and expression
analysis of the PEBP sequences led to the identification of an
orthologous \emph{TFL1} gene with a novel function. In addition, global
transcriptomic analysis revealed crucial transcription factors including
thermosensory and floral epigenetic genes involved in the initiation of
flowering in \emph{C. pallens} .

\textbf{Methods}

\textbf{Study species}

\emph{Chionochloa} \emph{pallens} (midribbed snow tussock) is an endemic
alpine New Zealand perennial grass. Plants grow as tussocks
(bunchgrasses) around 0.5-1.5 m tall and 15-50 cm in basal diameter.
These are very long-lived plants (\textgreater{} 100 years) with
discrete individuals. Each individual plant typically comprises hundreds
of tillers, each of which grows for four to five years in the field
prior to attaining reproductive maturity (Mark, 1965; Rees et al., 2002)
. Tillers may then wait some years before switching from vegetative to
reproductive, producing a flower stalk (or culm) and typically at least
one daughter tiller, before dying. Activation of the inflorescence and
floral development in \emph{C. pallens} occurs a year before flowering
(Mark, 1965, and see Appendix S1 in the supplementary information), so
we sampled leaves for genetic analysis from marked tillers and stored
the samples at -80 \selectlanguage{ngerman}°C until the fate of that tiller could be determined
up to a year later. Informative samples were then selected for RNA
analysis. The levels of flowering in \emph{C. pallens} vary markedly
between years (Kelly et al., 2013; Kelly et al., 2000). Previous
studies, including transplant experiments to different altitudes, have
shown that flowering in \emph{Chionochloa} is heavier after warm summer
temperatures in the year preceding flowering (Kelly et al., 2013;
Samarth et al., 2020).

\textbf{Study site and transplantation experiments}

In low-flowering years few or no tillers flower in the field, and even
in high-flowering years only a minority (\textless{}20\%) of tillers on
each plant flower (Rees et al., 2002). In order to improve our chances
of having both flowering and vegetative samples, we took leaf samples
from unmanipulated plants in the field, and from manipulated plants that
were transplanted to lower-elevation (warmer) or higher-elevation
(colder) sites to make flowering respectively more or less likely (Table
1). In 2016, unmanipulated \emph{Chionochloa pallens} plants at the main
Mt Hutt site at 1070 m elevation (43\selectlanguage{english}032' \selectlanguage{english}S, 1710 \selectlanguage{english}33' \selectlanguage{english}E) were selected
with leaf samples taken from three marked tillers on each of 10 marked
plants, with another 30 tillers selected and sampled the following two
years. None of the tagged plants at the control plot flowered in 2017
and 2018, but they did in 2019.

Several transplantation experiments to higher or lower altitudes were
carried out to manipulate flowering in \emph{C. pallens} . Each group of
transplanted tussocks was identified by the year of transplantation and
the treatment provided. Ten separate plants from the vicinity of the
control plot were moved to near sea-level at the University of
Canterbury (UC, Christchurch; 43\selectlanguage{ngerman}° 31\selectlanguage{english}' S, 172\selectlanguage{ngerman}° 35\selectlanguage{english}' E, 15 m a.s.l.) for
the inductive summer period in December 2016 to March 2017 (17Hot
transplants) and then transplanted back to the 1070 m site. Leaf samples
from tagged tillers (both transplants and control plants) were collected
at different time points through the year (January 2017, March 2017,
October 2017 and January 2018; between 11:00 am and 2:00 pm) and were
used for the gene expression studies. Leaf samples collected during the
inductive summer period (January 2017) were also used for the
transcriptomic analysis as described below. Two other sets of 10 plants
each were permanently transplanted in December 2015 from the control
site at 1070 m to different altitudes: to UC (16Hot) and to 1520 m on Mt
Hutt (16Cool). Leaf samples were collected at different seasonal
time-points (between 11:00 am and 2:00 pm) including summer (January
2016), autumn (March 2016), spring (October 2016) and autumn (post
flowering; March 2017).

In all the experiments, all leaf samples were collected and put directly
into dry ice within 20 seconds and stored at -80 \selectlanguage{ngerman}ºC until further
analysis. The tagged tillers were labelled and subsequent behaviour
(flowering or not) was recorded in the following year. The stored leaf
samples could then be identified as being from tillers that subsequently
flowered or remained vegetative, and suitable samples were selected for
downstream analysis.

\textbf{Phylogenetic, structural and functional analysis of the
PEBP-sequences in \emph{C. pallens}}

The reference `\emph{C.} \emph{pallens'} transcriptome assembly
published in Samarth et al. (2019) was used to identify the potential
homologues of the PEBP protein family (Table 2). The identified
homologous PEBP sequences were further clustered into their respective
sub-families using a phylogenetic approach. The PEBP-phylogenetic tree
consisting of 49 protein sequences (Table S1) was reconstructed using
the maximum likelihood method with Jukes Cantor substitution matrix and
1000 bootstrap replication using the PhyML package. Homologous PEBP
protein sequences were also modelled using a homology modelling approach
at the SWISS-PROT server and visualised using Pymol (student version).
For functional analysis, homologous PEBP-gene sequences (\emph{CpFT1,
CpFT2, CpFT3, CpFT4, CpFT5} and \emph{CpTFL1} ) were transformed
into\emph{Arabidopsis thaliana} ecotype Landsberg erecta \emph{ft-1}
mutant plants using the pB2GW7 vector. Wild-type Landsberg plants flower
after producing \textasciitilde{} 8-10 leaves whereas \emph{ft-1} is a
late-flowering mutant, flowering after \textasciitilde{}20 leaves
(Martinezzapater \& Somerville, 1990).

\textbf{RNA-extraction}

Three separate biological replicates for RNA sequencing and two separate
biological replicates for expression studies, each consisting of three
individual leaf samples each from three different plants, were ground
under liquid nitrogen using mortar and pestle. Each sample was dissolved
in 1 ml of Trizol reagent and heated at 65 ºC for 5 min. The samples
were then centrifuged at 13000 g for 5 min at 4 ºC. RNA was isolated
using the Qiagen Plant RNA extraction kit following the manufacturer's
instructions. The extracted RNA was further purified using a DNase I
digestion kit (Qiagen DNaseI kit) according to the product manual.
Extracted RNAs with 260/280 values of 2.0-2.2 and 260/230 valuse of
1.8-2.0 were selected for downstream analysis. Illumina TruSeq kit 2.0
was used to prepare cDNA libraries for paired-end sequencing on an
Illumina HiSeq 2500 platform. The sequencing was carried out at Macrogen
Inc. (South Korea).

\textbf{Transcriptomic analysis}

Transcriptomic analysis was performed comparing the tillers that
flowered in the next season (17Hot) and tillers that remained vegetative
(17Control) as described in Samarth \emph{et al} . (2019). 150 bp
generated paired-end reads were assembled into a single reference
transcriptome using the Trinity pipeline (Haas et al., 2013). Reads from
each of the samples were aligned to the reference assembly to generate a
count matrix using the Bowtie2.0 (Langmead \& Salzberg, 2012) followed
by the differential expression analysis using the DESeq2 package in R.
The significantly differentially expressed genes (adj.
p-value\textless{}0.05) were annotated based on orthologous proteins
identified in \emph{B. distachyon} using blastp with an E-value of
10\textsuperscript{-5} and 50\% protein identity. Gene ontology and
pathway enrichment analysis was performed based on the PANTHER server
(Mi et al., 2016). Validation of the RNA-seq results was performed using
RT-qPCR with a random selection of four genes from the transcriptomic
data (Appendix S2).

\textbf{Gene expression analysis}

Expression analysis was performed as described in Samarth \emph{et al} .
(2019). The primer sequences used are listed in Table S2. In addition
to\emph{CpFT1, CpFT2, CpFT3, CpFT4, CpFT5} and \emph{CpTFL1} ,
homologues of other floral-promoting genes known to regulate the
expression of\emph{FT-} like genes (Song \emph{et al.,} 2013) including
\emph{CpGI} ,\emph{CpHd1} , \emph{CpEhd3} , \emph{CpMADS1} ,
\emph{CpTPS1, CpMADS50} and a temperature regulator of flowering in
temperate grasses (\emph{CpVRN1} ) were also included in the expression
studies. Despite much effort, the expression of \emph{CpFT1} could not
be detected in the leaf samples.

\textbf{Statistical analysis}

Seasonal flowering expression of genes was analysed by two-way ANOVA
using the prism software with the following predictors: time-point,
site, and their interaction. The detailed statistical analysis is
summarised in Table S5. The comparative data between leaves from plants
that subsequently flowered and those from plants that remained
vegetative were analysed using two-way student's t-test.

\textbf{Results}

\textbf{Flowering in the tillers with leaf samples collected}

Control tagged plants at 1070 m did not flower in 2017 and 2018, but did
in 2019, a masting year. Since activation of floral development
initiates a year before anthesis (Appendix S1), leaf samples from these
control plants (18Control; January 2018) were included in the study.
Some tillers collected from the plants that had been moved to other
altitudes (16Hot, 16Cool, and 17Hot) flowered in the next season i.e. in
2017, 2017 and 2018, respectively. Flowering was recorded for each of
the tagged tillers and all the leaf samples were then correspondingly
identified as being from tillers that subsequently flowered or not
(Table 1).

\textbf{Orthologous PEBP-genes in \emph{C. pallens}}

Seven distinct homologues of PEBP-like gene family members were
identified from the draft transcriptome of \emph{C. pallens} (SRA
accession number: GHUI00000000) (Samarth et al., 2019). The GIHRV-domain
and LREY/HLHWIV-domain, both characteristic features of PEBP-family
proteins were found to be conserved in the homologous sequences
from\emph{C. pallens} (Fig. 1a).

The general topology of the reconstituted PEBP-protein family tree was
consistent with the previous findings of Karlgren et al. (2011) and Liu
et al. (2016), indicating that the PEBP phylogenetic tree can be
sub-divided into three major clades. These involve MOTHER OF FT and TFL1
(MFT)-like, FLOWERING LOCUS T (FT)-like, and TERMINAL FLOWER1
(TFL1)-like (Fig. 1b). The PEBP-protein family tree was consistent with
the species tree. Separate clades for homologous PEBP sequences
belonging to monocots and dicots can be seen in the phylogenetic tree.
All the \emph{C. pallens} sequences were found to clade with PEBP
protein sequences from species belonging to the Poaceae family. Of the
seven contigs identified, one belongs to the MFT cluster, one belongs to
the TFL1 group and five belong to the FT group (Fig. 1b). All sequences
grouped with functionally categorised members of their respective
families with high bootstrap values.

In \emph{C. pallens,} five of the seven contigs which clustered within
the FT group showed the presence of Tyr 85 residue, a critical amino
acid for flower promoting activity. These contigs were named
CpFT1-CpFT5, respectively (Fig. 1a). The external loop of FT (128-145
amino acid residues), critical for its florigenic activity (Ahn et al.,
2006), was conserved in all the \emph{C. pallens} FT sequences. All the
FTs from \emph{C. pallens} also had conserved characteristic features of
an FT protein including His87, Glu109, Arg139, and Gln140 near the
ligand-binding pocket. Structural modelling results indicate that Tyr85,
His87, and Glu109 can form hydrogen bonds and interact with the Arg139
residue (Fig. S1). However, the Gln140 residue usually found in FT-like
sequences has changed to His in CpFT3 and CpFT5, and to Pro in CpFT4
(Fig. 1a). The presence of proline at position 140 in CpFT4 may disturb
the ligand-binding wall and may render the protein ineffective.

One of the \emph{C. pallens} PEBP-like sequences clustering with the
TFL1 group has both critical amino acid indicators of a TFL1-like
sequence, a His88 and Asp140 (Fig. 1a). Segment B of exon 4 was also
seen to be highly variable in \emph{C. pallens} . During the genetic
complementation test in the \emph{ft-1} mutant, \emph{CpFT1, CpFT2,
CpFT3, CpFT4, CpFT5} and \emph{CpTFL1} were all found to accelerate
flowering. All the \emph{FT/TFL1} -like genes significantly shortened
the number of days for the complemented \emph{ft-1} plants to flower
under inductive conditions relative to the mutant (Fig. 1c). This
suggests that, in contrast to AtTFL1, which is a known floral repressor
(Ahn et al., 2006), the \emph{C. pallens} \emph{TFL1} -like gene
promotes the floral transition. Therefore, from here on \emph{CpTFL1} is
referred as the \emph{ANTI-TFL1} (\emph{CpATFL1} ). It is important to
point out that genetic complementation assays have been widely used by
the researchers to test the functionality of genes isolated from highly
diverged species (such as gymnosperms) into model plants including
Arabidopsis (Karlgren et al., 2011).

\textbf{Expression of \emph{ATFL1} correlates with the induction of
flowering in \emph{C. pallens}}

Leaf samples from the control plants at 1070 m and transplants that
flowered in the next season collected during and after the
transplantation period were analysed for the expression of selected
flowering-time genes (Table 1).

Leaf samples collected from both the 17Hot transplants that flowered in
2018 and control plants (17control) that had remained vegetative showed
a seasonal expression pattern (P \textless{} 0.05; Table S5; Fig. S2).
The seasonal expression patterns for \emph{CpFT3, CpFT4, CpFT5, CpATFL1,
CpVRN1, CpMADS50, CpMADS1} and \emph{CpHd1} (Fig. S2) were similar
between the control plants at 1070 m, all of which had remained
vegetative, and the transplants that flowered in the next season (Table
S5). Greater expression of \emph{FT-like} genes was observed in the
spring season (October samples) aligning with studies in model plant
species (Nagano et al., 2019). Expression of \emph{FT-like} genes was
similar during the spring season in both leaf sample sets, whether from
tillers that flowered in the next season or remained vegetative.
However, \emph{CpFT1} transcript was not detected in any of the samples
due to its expression either being below the limit of detection or
because it was not expressed in the leaves of the plant during the time
when leaf samples were collected. Expression analysis of
\emph{CpVRN1}(Fig. S2), another floral promoter, correlated with its
known seasonal expression pattern in temperate cereal species (Woods,
Ream, \& Amasino, 2014).

When the expression pattern is compared between the two sets of plants
during the inductive summer conditions (January 2017), \emph{CpATFL1}had
a significantly greater expression in the leaf samples of the 17Hot
transplants that flowered in the next season compared to the control
plants at 1070 m that remained vegetative. Along with
\emph{CpATFL1,}other floral promoters including \emph{CpTPS1, CpMADS1,
CpMADS50,} and\emph{CpVRN1} were also highly expressed in the leaf
samples associated with flowering tillers (Fig. 2a, Fig S3).

The expression of flowering-time genes in the leaf samples from the
transplanted plants that had flowered in 2017 (16Hot transplants and
16Cool transplants) was also compared to the control plants at 1070 m
(16Control), none of which flowered. All the gene(s) showed a seasonally
variable expression pattern (P \textless{} 0.001; Table S5) (Fig. S4).
The expression pattern of \emph{CpFT2} , \emph{CpFT3} , \emph{CpFT4}
,\emph{CpATFL1} , \emph{CpMADS1} , \emph{CpEhd3} and \emph{CpTPS1} in
the leaves of tillers that flowered in the next season at both the
sites, UC and 1520 m (16Hot transplants and 16Cool transplants,
respectively) showed a similar expression pattern to that observed in
the leaf samples associated with flowering in 17Hot transplants
(comparing Fig. S3 and Fig. S4). Even though \emph{CpFT5} had a seasonal
expression pattern, plants at UC showed greater expression during
autumn. All the genes showed a significant differential expression
between tillers which subsequently flowered vs tillers that remained
vegetative during the induction summer period (P \textless{} 0.001),
except for \emph{CpFT4}in the 16Cool transplants (Table S5).

Similar to the 17Hot transplants, \emph{CpATFL1} had a significantly
greater expression in the tillers that flowered at both the sites (UC
and 1520 m; 16Hot transplants and 16Cool transplants, respectively)
during the inductive summer period (January-2016) (Fig. 2a). Expression
analysis of \emph{CpVRN1} also correlated with its known seasonal
expression pattern with a higher peak in expression post winter (Shimada
et al., 2009). The expression of \emph{CpVRN1} was greater in the
tillers at both the sites (UC and 1520 m) that flowered in the next
season compared to the plants at the control site that had remained
vegetative (Fig. S3). Expression of \emph{CpHd1} , \emph{CpGI}
,\emph{CpEhd3} , \emph{CpMADS50} and \emph{CpMADS1} were all greater in
the leaf samples of the transplanted plants which subsequently flowered
compared to the control site plants that remained vegetative (Fig. S3).

Leaf samples associated with tillers that remained vegetative in both
the transplants, (16Hot transplant and 16Cool transplants) were found to
have significantly lower expression of \emph{CpATFL1} during the
inductive summer temperature (January 2016) compared to the leaf samples
from the same groups of transplants that flowered in the next season
(Fig. 2b). In addition to the transplants, leaf samples collected from
the control plants during January 2018 (18 Control) that flowered in the
masting year 2019, also showed a significantly greater expression
of\emph{CpATFL1} compared to the leaf samples from plants that remained
vegetative in the year 2019 (Fig. 2b). In summary, expression studies
suggest that an elevated expression of \emph{CpATFL1} during the
inductive summer temperature period is associated with the induction of
flowering in \emph{C. pallens} .

\textbf{Transcriptomic analysis identifies potential regulators
of\emph{CpATFL1}}

To study the transcriptional changes associated with the induction of
flowering in more depth, differential expression (DE) profiling, using
RNA-seq, was performed. Leaf samples (January 2017) from 17Hot
transplants located at UC which flowered heavily the next season were
compared to the control plants (17Control; January 2017) that remained
vegetative in the next season which was a non-masting year.

High-throughput 150 bp paired-end sequencing yielded 32 GB of raw data
with 120 million average read counts for each replicate. Reads were
assembled into a reference transcriptomic assembly using the Trinity
pipeline. The generated \emph{de novo} assembly yielded 140,826
transcripts, comprised of 383,092 contigs with an average length of
1428.92 bp and an N50 length of 2414 bp (Table S3). A total of 29,566
contigs (adj P \textless{} 0.01) were significantly differentially
expressed (DE) with 14,514 and 15,052 transcripts significantly up and
downregulated, respectively (Fig. 3).

\textbf{Gene Ontology analysis:} To gain insights into the function of
the genes that were DE, contigs were functionally categorised on the
basis of putative biological processes, molecular function, and cellular
localisation. Out of 29,566 DE contigs, 15,974 (54.09 \%) were annotated
against the \emph{B. distachyon} protein database with an E-value of
10\textsuperscript{-5}. The DE genes were further categorised based on
gene ontology using a hypergeometric test with a significance threshold
of 0.05 to identify key correlations between the internal cellular
activity and the phenotypic differences.

Upregulated genes in the leaf samples associated with tillers that
flowered in the next season were significantly enriched in the cellular
components belonging to the cytoplasm, organelle, and intracellular
organelle as the top three categories. Proteins encoded by the
upregulated genes were further clustered into separate functional
categories belonging to protein binding, transferase activity and anion
binding. These genes were found to be involved in biological processes
enriched in response to stimuli, oxidation-reduction processes and
cellular response to stimulus (Fig. 3d). Similarly, gene ontology
analysis was also carried out for the proteins encoded by the
downregulated transcripts. The downregulated transcripts were
significantly enriched in the cellular components assigned to the
cytoplasm, cell periphery and plasma membrane. These transcripts were
then further clustered into distinct molecular functions with most of
them belonging to transferase activity, anion binding, and small
molecule binding. Finally, these proteins were assigned to the category
of biological processes involved in organo-nitrogen compound metabolic
process, protein metabolic process, and biological regulation as the top
three classes (Fig. 3d).

2,786 downregulated transcripts when mapped to the KEGG database were
enriched in biosynthesis of secondary metabolites, metabolic pathways
and circadian rhythms with a false discovery rate of less than 0.05
(Fig. S5). About 3,788 (12.9\%) transcripts of the upregulated genes
were found to be associated with the KEGG pathways. The genes were
significantly enriched in metabolic pathways (44.1\%), biosynthesis of
secondary metabolites (26.1\%) and protein processing in endoplasmic
reticulum (4.75\%) as the top three categories.

\textbf{Differentially expressed orthologues of floral genes in \emph{C.
pallens}:} Out of the 29,567 DE contigs, 200 homologous floral protein
sequences (from \emph{A. thaliana} and \emph{B. distachyon} ) were
significantly differentially expressed in the leaves of the tillers that
flowered in the next season compared with tillers that remained
vegetative (Table S4). Floral integrator genes, including
\emph{CpMADS1}and \emph{CpATFL1} , were highly expressed in the tillers
that flowered in the next season which also aligns with the quantitative
PCR analysis performed earlier (Fig. 3c; Appendix S2). FRIGIDA
(\emph{CpFRI} ), a known floral repressor in \emph{A. thaliana} (Choi et
al., 2011) and\emph{B. distachyon} and other \emph{CpFRI} -interacting
genes were downregulated in the tillers that flowered in the next season
(Table S4).

Leaf samples from 17Hot transplants that flowered in the next season
also showed an increase in the expression of thermosensory genes
including \emph{CpPIF4} , \emph{CpPIF5} and \emph{CpbHLH80} relative to
plants at the control site. These genes also act as floral promoters in
response to high temperatures (Kumar et al., 2012). \emph{CpSPL15} a
known floral promoter in perennial plants (Hyun et al., 2019), was also
upregulated in the 17Hot transplants compared to the control
plants.\emph{CpVRN2} , another temperature regulated floral repressor
(Yan et al., 2004) was also downregulated in the tillers during the
inductive summer period that subsequently flowered (Fig. 3c).

Two gibberellin catabolism gene family members (\emph{CpGA2ox1}
and\emph{CpGA2ox8} ) were also downregulated, while genes involved in
gibberellin synthesis, including \emph{CpKS} (kaurene synthase)
and\emph{CpGA20ox2} were upregulated in the tillers that subsequently
flowered. Gibberellins have been shown to promote flowering in plants
either by the activation of \emph{FT} through SPL-family proteins or by
activation of \emph{SOC1} , independent of FT (Yu et al., 2012).

Several epigenetic editor genes, known to deposit active methylation
marks to activate the expression of flowering promoting genes,
including\emph{CpREF6} , \emph{CpMSI1} , \emph{CpFLD} , and \emph{CpEBS}
(He, 2012), were upregulated in the tillers that flowered in the next
season compared to the tillers from the plants at the control site that
had remained vegetative (Table S4). Additionally, genes involved in the
epigenetic repression of floral repressors such as \emph{CpFLK}
,\emph{CpFY} , \emph{CpFPA} and \emph{CpVEL1} (Qüesta, Song, Geraldo,
An, \& Dean, 2016) were also upregulated in the tillers that
subsequently flowered (Table S4).

\textbf{Discussion}

\emph{C. pallens} is a non-model monocot species with a large genome (2n
= 42) (Connor, 1991). Due to highly variable flowering in \emph{C.
pallens} growing in natural conditions, it would have been difficult to
track the induction of the flowering process using only unmanipulated
plants. Transplanting plants to a lower altitude (15 m, University of
Canterbury) and warmer temperature conditions induced heavy flowering
in\emph{C. pallens} , as previously reported for \emph{C. rigida} (Mark,
1965), allowing sample collection during the inductive summer period and
enabling us to study the molecular regulators of flowering. With recent
advances in sequencing technologies, transcriptomic profiling of
non-model plant species has been demonstrated to be advantageous and
effective when studying ecological phenomena (Todd et al., 2016). In the
present study, use of ecological transcriptomics resulted in the
identification of key orthologous flowering-time gene(s) in \emph{C.
pallens} responsible for regulating the floral transition.
Transcriptomic profiling of different orthologous floral protein
sequences showed that many components of the flowering pathways
identified in \emph{A. thaliana} and \emph{B. distachyon} were also
conserved in \emph{C. pallens} . For example, \emph{VRN} genes, which
are known to regulate the floral transition in response to vernalisation
and temperature in temperate grasses (Ream et al., 2014), were also
identified successfully using the global transcriptomic approach.

Phylogenetic characterisation followed by expression profiling of
the\emph{PEBP} -genes resulted in characterisation of the floral
promoting genes. Like maize, which has 26 homologous PEBP sequences (Liu
et. al.\emph{,} 2016), \emph{C. pallens} has multiple copies of PEBP
gene sequences. This may be attributed to the polyploid nature of the
plant, along with gene duplication events. The identified PEBP-like
sequences from \emph{C. pallens} were categorised into their
subfamilies. Five of the sequences were shown to be FT-like sequences.
Most of the sequences grouped with sequences which have already been
functionally characterised, which aided in predicting function. All of
the\emph{CpFT-like} genes showed similar seasonal expression patterns
(except for \emph{CpFT1} ), with their greatest expression during the
spring season. This pattern of expression is similar to the expression
of \emph{FTs} characterised in other plant species (Nagano et al., 2019)
but none of them could be correlated with the induction of the floral
transition in the tillers that flowered in the next season. This could
be due to the fact that the activation of the floral transition
in\emph{C. pallens} occurs during summer rather than in spring when the
expression of the \emph{FT-like} genes was at its peak.

\textbf{CpATFL1 acts as a promoter of flowering in \emph{C. pallens}}

The TFL1 protein is generally a floral repressor in annual and perennial
plants, acting antagonistically to FT (Liu et al., 2016).\emph{CpATFL1,}
clustering with the TFL1 clade in the \emph{PEBP} -gene family tree,
also had the key conserved amino acid signatures of the TFL1 protein
including His88, Asp144, and a variable segment B of exon 4 (Fig. 1a).
However, functional analysis involving genetic complementation of the
\emph{ft-1}mutant of \emph{A. thaliana} (Ler) with \emph{CpATFL1}
indicated that\emph{CpATFL1} was acting as a floral promoter: the
\emph{ft-1} mutants complemented with the \emph{CpATFL1} gene flowered
significantly earlier than the wild type \emph{A. thaliana} plants. This
response prompted a computational biology investigation of
\emph{CpATFL1} . Sequence comparison of \emph{CpATFL1} with close
relatives of \emph{FT} in other Poaceae members including \emph{ZCN7}
and \emph{ZCN8} (Lazakis, Coneva, \& Colasanti, 2011) suggested that
there is a conserved residue of Gln150 between two different gene
sequences. Ho \& Weigel (2014) showed that position 150-152 is conserved
in \emph{FT} -like sequences and is crucial for floral promoting
activities (Fig. 4\textbf{a, b} ), whereas these positions are much more
variable in \emph{TFL1} -like sequences. Position 150 in \emph{TFL1}
-like sequences is generally occupied by either Glu or Asp as observed
in \emph{ZCN1} and \emph{OsRCN4} , homologues of \emph{TFL1} in maize
and rice respectively (Fig. 4b) (Ho \& Weigel, 2014).

The change in Glu 150 to Gln 150 in \emph{CpATFL1} is caused by a single
base-pair change from GAA to CAA. Glu is a negatively charged amino acid
while Gln is a positively charged amino acid (Fig. 4c). Ho \& Weigel
(2014) showed that altering the surface charge of \emph{FT}
and\emph{TFL1} -like sequences can alter their corresponding activities.
When analysed for the surface charge potential compared to ZCN8 (an FT
homologue in maize) and ZCN1 (a TFL1 homologue in maize), CpATFL1 showed
a similar charge distribution to ZCN8. The surface surrounding position
150 was strongly positive in ZCN8 and CpATFL1, whereas the surface
around that position in ZCN1 was found to be highly negative (Fig. 4d).
This change in the surface charge could be the reason for the flower
promoting activity of CpATFL1, in contrast to the usual TFL1 activity.

Our gene expression studies also support the hypothesis
that\emph{CpATFL1} is associated with the induction of flowering in
\emph{C. pallens} (Fig. 2). Elevated expression of \emph{CpATFL1} was
observed during the inductive summer condition in the leaf samples from
the plants that subsequently flowered in all the transplants and plants
growing at the control site (1070 m) in natural conditions. It is
interesting to point out that generally expression of \emph{TFL1} -like
proteins has been restricted to apical meristems (Liu et
al\emph{.,}2016). However, recent studies have shown that
homologous\emph{TFL1} -like proteins are expressed in the leaves of a
number of perennial plants including \emph{Arabis alpina, Rosa
chinensis, Fragaria vesca, Jatropha curcus, Chrysanthemum morifolium}
and \emph{Manihot esculenta} (Adeyemo, Hyde, \& Setter, 2019; Gao et
al., 2019; Iwata et al., 2012; Li et al., 2017; Wang et al., 2011). The
specifics of the regulation of \emph{TFL1} in perennial plant species is
still relatively unknown, except for \emph{A. alpina} .

\textbf{Potential regulators of \emph{CpATFL1} initiate the floral
transition in \emph{C. pallens}}

Global expression analysis of the leaves collected during the summer
inductive conditions from tillers that subsequently flowered shows
upregulation of crucial genes including \emph{CpFTIP1} and
\emph{CpFDP}that are involved in the transport of florigen-like
molecules from the leaves to the apex to subsequently activate the
floral meristem gene(s) through formation of the florigen activation
complex (FAC) (Kaneko-Suzuki et al., 2018). Calcium is also required to
catalyse the formation of the florigen activation complex (Kawamoto,
Sasabe, Endo, Machida, \& Araki, 2015).The expression of \emph{CpCPK6} ,
a kinase required for calcium signalling, was also upregulated in the
tillers that flowered in the next season. It is important to acknowledge
that formation of FAC occurs at the shoot apical meristem. Even though
the differential expression of these genes was observed in the leaves,
it may suggest that CpATFL1 may form the FAC through the activity of
CpFTIP1 and CpFDP, catalysed by CpCPK6 at the shoot apical meristem.
Further investigation involving a yeast two hybrid assay or bimolecular
fluorescence assay would reveal whether CpATFL1 and CpFTIP1 do indeed
interact and could, therefore, transport ATFL1 to the apex.

Vernalisation responses to mediate flowering-time control in temperate
monocots are controlled by the \emph{VERNALISATION (VRN)} loci (Reams et
al\emph{.,} 2014). The \emph{VRN2} locus encodes a CCT-domain protein
that blocks the floral transition. Exposure to cold temperatures
increases the expression of \emph{VRN1} , a repressor of \emph{VRN2}
(Yan et al\emph{.,} 2004) and maintains the repressive state of
\emph{VRN2}even after vernalisation. Repression of \emph{VRN2} leads to
activation of \emph{VRN3} , a homologue of \emph{FT} and \emph{Hd3a,}
after plants are exposed to warm spring temperatures (Preston \&
Kellogg, 2008; Shimada et al., 2009; Woods, et al., 2016).
Transcriptomic analysis showed an elevated expression of \emph{CpVRN1}
in the tillers that flowered during the increase in summer temperature,
which is also observed in other temperate cereals during the process of
floral transition (Trevaskis, 2010). On the other hand, expression
of\emph{CpVRN2} , the repressor of \emph{FT} -like genes (Alexandre \&
Hennig, 2007), was downregulated in the same samples. Greater expression
of \emph{CpVRN1} during the spring season and which remained elevated
through the summer may have blocked repressive signals from\emph{CpVRN2}
. This repression was potentially maintained until the next summer
resulting in the activated transcription of \emph{CpATFL1} to induce
flowering.

Transcriptomic analysis also revealed an increase in the expression of
thermosensory genes, including \emph{CpPIF4} and \emph{CpPIF5} in the
tillers associated with flowering in the next season. \emph{PIF} genes
are known to regulate responses to high temperatures (Choi \& Oh, 2016)
and are involved in the activation of the flowering process along with
similar bHLH floral promoting proteins such as bHLH76 and bHLH80 (Ito et
al., 2012). In addition to \emph{PIF} -family genes, homologues
of\emph{bHLH76} and \emph{bHLH80} in \emph{C. pallens} , were also
upregulated in the above samples, which may interact with CpPIF4 to
activate \emph{ATFL1} . Transient assays are further required to confirm
this interaction. The summer temperature cue may also have blocked the
expression of several floral repressors including homologues
of\emph{AP2-LIKE} genes and \emph{SVP} (Capovilla, Schmid, \& Pose,
2015; Mateos et al., 2015).

Emphasis was also placed on the role of epigenetic modifiers known to
regulate the reproductive transition in plants. Ambient temperatures
have been shown to regulate the expression of floral repressors via
epigenetic modification including either through deposition of
repressive histone marks (to suppress gene expression) such as H3K27me3
or by removing acetyl groups from the histone tails at the gene loci
(Bratzel \& Turck, 2015; He, 2012). In contrast, high temperatures have
been shown to lead to deposition of active histone marks (H3K4me3 or
H3K36me3) at the loci of floral promoters (Avramova, 2015).\emph{CpREF6}
and \emph{CpFLD} , homologues of \emph{REF6} and \emph{FLD}which are
reported to promote flowering (He, 2012; Lu, Cui, Zhang, Jenuwein, \&
Cao, 2011), were upregulated in the leaves of plants that flowered in
the next season. CpFLD may then interact with CpHDA6, a histone
deacetylase complex, activating the floral promoting genes, a process
well established in Arabidopsis (Yu, Chang, \& Wu, 2016). Homologues of
\emph{CpFLK} , \emph{CpFVE} , and \emph{CpFY} , genes known to repress
the expression of floral repressors epigenetically (Cho, Yoon, \& An,
2017; He \& Amasino, 2005), were upregulated in the tillers that
subsequently flowered. Interestingly, homologues of epigenetic editors,
including \emph{CpVEL1} and \emph{CpMSI1} , which are known to be
involved in the activation of \emph{VRN1} and \emph{SOC1} , respectively
through deposition of active histone marks (Higgins, Bailey, \& Laurie,
2010; Oliver \& Finnegan, 2011) were also upregulated in the tillers
that flowered in the next season. This may suggest that an external
signal such as summer temperatures may lead to certain epigenetic
changes enabling the transcription of \emph{CpATFL1} to promote
flowering.

\textbf{Endogenous signals are also necessary to initiate flowering
during inductive conditions}

Transcriptomic analysis also showed an increased expression of genes
involved in gibberellin synthesis in the leaf samples associated with
flowering tillers. Previous studies have already shown that gibberellin
is able to initiate and accelerate flowering in \emph{Chionochloa} sp.in
the glasshouse (Martin, Jameson, Mark, Yeung, \& Pharis, 1993) and the
field (Turnbull et al., 2012). Gibberellin has also been shown to
upregulate the expression of \emph{FT} and \emph{SOC1} independently of
the photoperiodic pathway in Arabidopsis and several temperate cereals
as well (Yu et al., 2012). Greater expression of gibberellin synthesis
gene(s) may have promoted flowering in \emph{C. pallens} by escalating
the expression of \emph{CpATFL1} and \emph{CpMADS1} as observed in the
transcriptomic studies. Along with gibberellin, expression of the sugar
signalling genes, \emph{CpUGT87A2} , \emph{CpTPS1} and \emph{CpHXK1} ,
was found to be significantly greater in the leaves of the tillers that
flowered in the next season. Appropriate sugar levels are required as an
internal standard for a plant to respond to environmental signals to
induce the floral transition (Yang, Xu, Koo, He, \& Poethig, 2013).

These data emphasise that, along with the external signals, \emph{C.
pallens} may require sufficient internal signals to respond and initiate
the floral transition. This may explain why certain tillers in the
experimental transplants did not undergo flowering even following the
perception of an external cue such as warm summer temperatures. These
vegetative tillers may be either too young or may be still in process of
accumulating enough resources.

\textbf{Hypothesised molecular model of flowering in \emph{C. pallens}}

This study showed a strong conservation of the activity of PEBP-like
gene family members as floral promoters in \emph{C. pallens} .
Transcriptomic analysis further suggested that \emph{C.
pallens}undergoes flowering through the interaction of external and
internal signals which can be integrated at the \emph{CpATFL1} loci to
initiate flowering.

We suggest that flowering in the masting plant \emph{C. pallens} is a
coordinated two-step process (Fig. 5). In the first stage, winters may
suppress the activity of \emph{CpVRN2} by increasing the expression
of\emph{CpVRN1} , thereby elevating the expression of \emph{FT} -like
genes in the spring, each year. However, an increase in the FTs does not
necessarily correlate with flowering in plants in the next season. In
the second stage, only plants with sufficient internal cues and
consequently a greater expression of \emph{CpATFL1} may be able to
respond to the floral transition and, subsequently, flower. This
two-stage process almost certainly requires additional epigenetic
factors acting on \emph{CpATFL1} to control the reproductive transition.
Further analysis based on CHiP-seq may allow the identification of the
specific epigenetic genes and the corresponding methylation changes in
the histones to activate the flowering process in response to the
environmental cues.

Because of the potential impacts of global climate change on masting
phenology (Monks et al., 2016), and the potential down-stream impacts on
introduced and native fauna (Griffiths \& Barron, 2016), it is becoming
increasingly important to accurately predict masting years. Such
predictions are strongest when they are based on a detailed mechanistic
understanding of the underlying control mechanisms. The current study
has identified significant molecular regulators of flowering in \emph{C.
pallens} which can be used to explore changes in flowering gene
expression under altered climates. This will allow the design of new
masting models with greater confidence and to understand how mast
flowering may change in the face of climate change.

\textbf{Acknowledgements}

The authors acknowledge the Marsden Fund of the Royal Society of New
Zealand for providing financial support (Grant UOC1401). The authors
acknowledge the IT facilities of the University of Canterbury and
University of Otago for providing the computational facilities and
analysis tools.

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\textbf{Data accessibility statement}

Accession numbers for the raw sequencing reads submitted to SRA
database:

SAMN12138067, SAMN12138068, SAMN12138069, SAMN12138070, SAMN12138071,
SAMN12138072

\textbf{Author contributions}

Design of the research -- Samarth, PEJ, DK and MT

Performance of the research- Samarth and RL

Data analysis- Samarth, RL, RM and DK

Collection, or interpretation- Samarth, RL, RM, AMP, MT, DK and PEJ

Writing the manuscript- Samarth, DK, PEJ, MT and RM

TABLES

Table 1. \emph{Chionochloa pallens} plants used for leaf sample
collection in this study. All plants were initially growing at 1070 m on
Mt Hutt. For each row, we took leaf samples from 3 tagged tillers. from
each of 10 tagged plants\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Name & Site & Treatment & Year collected & Fate of sampled tillers the
next year\tabularnewline
\midrule
\endhead
16Control & Hutt 1070 m & unmanipulated & 2016 & vegetative
only\tabularnewline
17Control & Hutt 1070 m & unmanipulated & 2017 & vegetative
only\tabularnewline
18Control & Hutt 1070 m & unmanipulated & 2018 & flowering and
vegetative\tabularnewline
17Hot & UC 15 m & moved to 15 m for summer then back to 1070 m & 2017 &
flowering and vegetative\tabularnewline
16Hot & UC 15 m & moved to 15 m & 2016 & flowering and
vegetative\tabularnewline
16Cool & Hutt 1520 m & moved to 1520 m & 2016 & flowering and
vegetative\tabularnewline
\bottomrule
\end{longtable}

Table 2: List of flowering pathway genes selected for expression
analysis in \emph{C. pallens}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Gene & \emph{A. thaliana} homologue & \emph{A. thaliana} locus ID & Rice
homologue & Rice protein ID & \% Identity & E-value & \%Gaps & Contig
ID\tabularnewline
\emph{Cp GI} & \emph{GIGANTEA} & \emph{AT1G22770} & \emph{OsGI} &
CAB56058.1 & 95.24 & 0 & 0.00E+00 &
TRINITY\_DN35492\_c2\_g6\_i1\tabularnewline
\emph{Cp Ehd3} & & & \emph{OsEhd3} & BAI77463.1 & 78.35 & 3.27E-52 &
0.00E+00 & TRINITY\_DN32679\_c1\_g8\_i1\tabularnewline
\emph{Cp MADS50} & & \emph{AT2G45660} & \emph{OsMADS50} & Q9XJ60.1 &
78.39 & 0.00 & 1.4 & TRINITY\_DN25163\_c1\_g3\_i4\tabularnewline
\emph{CpHd1} & \emph{CO} & \emph{AT5G15840} & \emph{OsHd1} & BAB17628.1
& 73.06 & 1.00E-93 & 0 & TRINITY\_DN28069\_c0\_g1\_i3\tabularnewline
\emph{CpTPS1} & \emph{TPS1} & \emph{AT1G78580} & \emph{OsTPS1} &
BAG89812.1 & 86.09 & 0.00E+00 & 0 &
TRINITY\_DN51229\_c0\_g1\_i1\tabularnewline
\emph{CpMADS1} & \emph{SOC1} & \emph{AT2G45660} & \emph{ZmMADS1} &
NP\_001105152.1 & 78.8 & 8.50E-99 & 2.17E+00 &
TRINITY\_DN25163\_c1\_g3\_i4\tabularnewline
\emph{CpFT2} & \emph{FT} & \emph{AT1G65480} & \emph{OsHd3a} & BAB61030.1
& 85 & 3.90E-63 & 0.00E+00 & TRINITY\_DN32350\_c0\_g3\_i1\tabularnewline
\emph{CpFT3} & \emph{FT} & \emph{AT1G65480} & \emph{OsHd3a} & BAB61030.1
& 62.75 & 1.26E-68 & 6.50E-01 &
TRINITY\_DN108898\_c0\_g1\_i2\tabularnewline
\emph{CpFT4} & \emph{FT} & \emph{AT1G65480} & \emph{OsHd3a} & BAB61030.1
& 72.9 & 4.09E-57 & 0.00E+00 &
TRINITY\_DN37301\_c2\_g2\_i1\tabularnewline
\emph{CpFT5} & \emph{FT} & \emph{AT1G65480} & \emph{OsHd3a} & BAB61030.1
& 66.06 & 6.13E-51 & 0.92 & TRINITY\_DN61628\_c0\_g1\_i3\tabularnewline
\emph{CpVRN1} & \emph{VRN1} & \emph{AT3G18990} & \emph{BdVRN1} &
XP\_010237091.1 & 86.08 & 3.57E-84 & 0.00E+00 &
TRINITY\_DN37693\_c0\_g1\_i1\tabularnewline
\emph{CpATFL1} & \emph{TFL1} & \emph{AT5G03840} & \emph{ZCN1} &
ABW96224.1 & 82 & 0 & 1.60E-01 &
TRINITY\_DN91976\_c0\_g2\_i1\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1: \textbf{(a)} \textbf{Multiple sequence alignment of PEBP-like
protein sequences.} Sequences were aligned using the MUSCLE alignment
tool. The red asterisks indicate the conserved and key amino acid
residues required for the corresponding activity of FT and TFL1-like
sequences. The band at the bottom of the alignment represents the
percentage of conservation of the amino acids at a position. Red
indicates stronger conservation while blue represents variable
sequences. \textbf{(b)} \textbf{Phylogenetic reconstruction of the
PEBP-gene family with homologous \emph{C. pallens} sequences} . The
evolutionary history was inferred by using the Maximum Likelihood method
based on the JTT matrix-based model. The bootstrap consensus tree
inferred from 1000 replicates is taken to represent the evolutionary
history of the taxa analysed. Aly: \emph{Arabidopsis lyrata} subsp.
lyrata, Ath: \emph{Arabidopsis thaliana} , Ac: \emph{Allium cepa,}
Bv:\emph{Beta vulgaris,} Bd: \emph{Brachypodium} \emph{distachyon,}
Cs:\emph{Chrysanthemum seticuspe} , Chm: \emph{Chrysanthemum X
morifolium,}Gm: \emph{Glycine max,} Ha: \emph{Helianthus annuus,}
Nt:\emph{Nicotiana} \emph{tabacum} , Osa: \emph{Oryza sativa,}
Ptr:\emph{Populus trichocarpa,} ZCN: \emph{Zea mays} and \emph{C.
pallens} . Branches in red colour represent the MFT clade, blue
represent theTFL1 clade and green represent the FT clade. \textbf{(c)}
\textbf{Functional characterisation of the \emph{C. pallens} PEBP- like
sequences.} The number of rosette leaves of the transgenic and the
wild-type plants at flowering (mean \selectlanguage{ngerman}± SD, n = 10). (Significance of
transgenic and control plants compared to Ler \emph{ft-1} , Student's
t-test *** P \textless{} 0.001, ** P \textless{}0.01).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2: \textbf{Gene expression analysis} of the orthologous
PEBP-sequences in \emph{C. pallens} during the inductive summer period
in the plants (a) control plants at 1070 m in 2016 and 2017 (16 control
and 17 control), plants translocated to UC for summer (December
2016-March 2017) (17Hot transplants) that flowered in 2018, plants
transplanted to UC (16Hot transplants) and 1520 m Mt Hutt (16Cool) that
flowered in 2017 (b) expression of \emph{CpATFL1} in the leaves of the
tillers that either flowered or remained vegetative at distinct
altitudes including UC (15 m), 1520 m Mt Hutt and control plot (1070 m
Mt Hutt). The data are represented by the mean \selectlanguage{ngerman}± S.D. of two biological
replicates each with three technical replicates. The x-axis indicates
the transplanted site from where leaf samples were collected and the
fate of the plant either remaining vegetative (veg) or flowering (flw)
the next season. The data were calculated using relative fold changes
against two internal reference genes, \emph{CpExP} and \emph{CpTHP} .
(two-way ANOVA, ** P \textless{} 0.01, * P \textless{} 0.05; Table S5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 3: (a) \textbf{Heat-map of 50 most significantly differentially
expressed genes in the leaves of 17Control and 17Hot transplants
(17H)}collected during the inductive summer period (January-2017). The
heatmap was constructed using the R packages \emph{DESeq2} and
\emph{heatmap.2} . Hierarchical clustering is based on the Spearman's
coefficient calculated from the log-transformed RNA-seq data. (b)
\textbf{PCA plot}of the transcriptomic data from three separate
biological replicates, each for 17 control and 17Hot transplants. (c)
\textbf{Differentially expressed flowering-pathway genes in \emph{C.
pallens.}} Logarithmic (log\textsubscript{2}) fold change in the
orthologous floral genes found to be significantly differentially
expressed in the leaves of the 17Hot transplants that flowered in the
next season relative to the leaf samples from the control plot plants
that remained vegetative. (d)\textbf{Gene Ontology analysis.} GO
classification of the upregulated genes (left-hand side) and
downregulated genes (right-hand side) in the leaves of the 17Hot
transplants that flowered compared to the leaves from the plants at the
control plot with a significance level of 0.05 after the Benjamini and
Hochberg FDR correlation. (BP: Biological Process, CC: Cellular
Component, MF: Molecular Functions)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 4: (a) \textbf{Molecular structure of CpATFL1.} The protein
structure was built based on the \emph{ab initio} approach from the
QUARK server. The highlighted pink region represents the conserved
critical amino acids for the activity of TFL1 as a repressor with a
reference amino acid, Phe at the position 120, situated towards the
bottom region. (b) \textbf{Multiple sequence alignment of FT and TFL1
homologues in \emph{A. thaliana} (ATH), \emph{Zea mays} (ZCN), \emph{O.
sativa} (Os), \emph{B. distachyon} and \emph{C. pallens}.} Sequences
were aligned using the MUSCLE alignment tool. The level of conservation
of amino acids is represented by the colour graph at the bottom of the
alignment. Red represents stronger conservation while black represents
variable amino acid sites. The red asterisk represents the critical
amino acid change (Glu-\textgreater{} Gln) in CpATFL1 which may be
responsible for its floral-promoting activity. (c) \textbf{Codon usage
between ZCN1, a TFL1 homologue in \emph{Z. mays}, and CpATFL1.} *
represents the single nucleotide change coding for amino acid at
position 150. (d) \textbf{Electrostatic surface charge for ZCN8, ZCN1
and CpATFL1} . Electrostatic surface potential for each sequence was
calculated from the CHARMM server. Protein structures were viewed using
the pymol represented by the amino acid at position 150 and a reference
amino acid, Phe, at position 120 to orient the structures.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 5:~ \textbf{Hypothesised} \textbf{flowering network in \emph{C.
pallens}} . In \emph{C. pallens} , \emph{ATFL1} acts as the key floral
integrator gene. During non-inductive conditions, homologues of floral
repressors including FRI-like, AP2 and SVP can block the process of
floral transition. Photoperiodic (GI, CO) and vernalisation (VRN1, VRN2)
pathways may interact to activate the expression of \emph{FT} -like
genes during inductive conditions every year. However, only plants with
sufficient internal cues (e.g. sucrose content (\emph{TPS1} ), maturity
(\emph{SPLs} ) and gibberellin (GA)) are able to respond to the external
signals such as summer temperatures (PIF4,5) which can then elevate the
expression of \emph{CpATFL1} to induce flowering in the following
season. The yellow boxes indicate homologues of the floral integrator
genes identified in \emph{C. pallens.} The solid lines indicate
co-expression of genes based on the transcriptomic data and known
literature, while question mark indicates a hypothetical co-regulation
between two genes which may exist in model species but have not been
discovered yet. Genes represented with the black and the white ink,
respectively represent the up- and down-regulation in the expression of
corresponding genes observed in the tillers that flowered in the next
season compared to tillers that remained vegetative.

\selectlanguage{english}
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