the evolutionary impact of autopolyploidy in tolmiea...
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THE EVOLUTIONARY IMPACT OF AUTOPOLYPLOIDY IN TOLMIEA (SAXIFRAGACEAE)
By
CLAYTON J. VISGER
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2017
© 2017 Clayton J. Visger
To my fiancée Katrin and my mother for their support, and to my late father who has forever been my hero
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ACKNOWLEDGMENTS
I am extremely grateful to my advisors Douglas E. Soltis and Pamela S. Soltis,
for their support, encouragement, and most importantly, their patience. I thank the
members of my committee, Emily B. Sessa and Matias Kirst, for their valuable feedback
and aid with my dissertation work. I am indebted to Shannon S. Datwyler for her
mentorship, continued support, and for starting me down the academic path. I also
thank Michael Chester for his friendship, mentorship, and the countless hours spent
discussing all facets of polyploidy. I am thankful to Charlotte Germain-Aubrey, Maya
Patel, and Gane K-S. Wong, for their invaluable collaborative efforts on the dissertation
work presented here. I thank those who I have collaborated with beyond my
dissertation work—in particular Nicolas W. Miles, Andrew A. Crowl, Nico Cellinese,
Evgeny V. Mavrodiev, Paul G. Wolf and Carol A. Rowe. I am also thankful to my lab
mates and colleagues at the University of Florida, Richard Hodel, Shan Shengchen,
Rebbeca L. Stubbs, Jacob B. Landis, Youl Kwon, Xiaoxian Liu, Dr. Jov, and especially
Mathew A. Gitzendanner for everything that he does. Finally, I thank D. Blaine
Marchant, Gregory W. Stull, Daniel Sasson, and Tim Crombie who made graduate
school an endeavor to remember.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
ABSTRACT ................................................................................................................... 10
CHAPTER
1 GENERAL INTRODUCTION .................................................................................. 12
2 NICHE DIVERGENCE BETWEEN DIPLOID AND AUTOTETRAPLOID TOLMIEA (SAXIFRAGACEAE) .............................................................................. 18
Introduction ............................................................................................................. 18 Materials and Methods............................................................................................ 23
Sampling .......................................................................................................... 23
Flow cytometry ................................................................................................. 23 Ecological niche modeling (ENM) ..................................................................... 24
Niche overlap ................................................................................................... 25 Environmental principal component analysis (PCA) ......................................... 26 Physiological response to changes in soil moisture ......................................... 26
Guard cell measurements ................................................................................ 27 Canopy transmittance ...................................................................................... 27
Results .................................................................................................................... 28 Flow cytometry ................................................................................................. 28
ENM and niche overlap .................................................................................... 28 Environmental PCA .......................................................................................... 29 Physiological response to changes in soil moisture ......................................... 30
Guard cell measurements ................................................................................ 30 Canopy transmittance ...................................................................................... 30
Discussion .............................................................................................................. 31 Conclusion .............................................................................................................. 36
3 DIVERGENT GENE EXPRESSION LEVELS BETWEEN DIPLOID AND AUTOTETRAPLOID TOLMIEA RELATIVE TO THE TOTAL TRANSCRIPTOME, THE CELL, AND BIOMASS. .................................................. 46
Introduction ............................................................................................................. 46 Materials and Methods............................................................................................ 51
Results .................................................................................................................... 55 Discussion .............................................................................................................. 57
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4 DIFFERENTIAL DROUGHT RESPONSE AND TRANSCRIPTOME SIZE PLASTICITY BETWEEN DIPLOID AND AUTOPOLYPLOID TOLMIEA ................. 75
Introduction ............................................................................................................. 75
Materials and Methods............................................................................................ 80 Results .................................................................................................................... 83 Discussion .............................................................................................................. 85
Variation in the transcriptomic response to drought stress ............................... 86 Differential drought response (DDR) ................................................................ 88
Caveats and conclusions.................................................................................. 92
5 GENERAL CONCLUSIONS ................................................................................. 101
APPENDIX
A BIOCLIMATIC RESPONSE VARIABLES ............................................................. 107
B ENVIRONMENTAL SPACE PCA ......................................................................... 108
LIST OF REFERENCES ............................................................................................. 109
BIOGRAPHICAL SKETCH .......................................................................................... 124
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LIST OF TABLES
Table page 2-1 Population locality, ploidal level, elevation, and canopy transmittance of
diploid and tetraploid Tolmiea. Population vouchers are housed in the California State University, Sacramento herbarium (SACT). .............................. 37
2-2 Linear mixed-effects model results for canopy transmittance and physiological response. ...................................................................................... 39
2-3 Guard cell size vs ploidy ..................................................................................... 40
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LIST OF FIGURES
Figure page 2-1 Sampling map of the Pacific Northwest. All data points, excluding those in
green, were used for niche model generation. Circles represent data mined from herbaria, while stars represent field-collected accessions with ploidy verified by flow cytometry (FCM). ....................................................................... 41
2-2 Niche suitability across the Pacific Northwest for T. diplomenziesii (blue) and T. menziesii (red) categorized by climatic suitability score as low (0.15–0.3), moderate (0.3–0.5), and high (> 0.5). The dashed line indicates the realized North/South range break for T. diplomenziesii and T. menziesii. ....................... 42
2-3 Observed niche overlap (red dashed line – 0.140) vs. a distribution of 100 niche overlap scores generated under the null assumption of niche equivalency (A) and similarity (T. diplomenziesii vs. T. menziesii, and T. menziesii vs. T. diplomenziesii, B and C, respectively). ..................................... 43
2-4 Tolmiea diplomenziesii (blue) and T. menziesii (red) populations plotted against temperature-related variables (PC1) and precipitation-related variables (PC2). Ellipses represent 90% confidence. ......................................... 44
2-5 Log soil moisture vs. intrinsic water-use efficiency (top), log leaf water content (middle), and log leaf water potential (bottom). Regression lines denote a significant effect on the y-axis due to log soil moisture (gray) or ploidy by soil moisture interaction (colored by species). ..................................... 45
3-1 A simplified example of how spike-in standards can be used during read normalization to enable comparisons of expression level at different biological scales between a hypothetical diploid-polyploid pair with differing cell density. ......................................................................................................... 66
3-2 Generalized distributions of Tolmiea menziesii and Tolmiea diplomenziesii. The population sources for plants used in this study are represented as red triangles (T. diplomenziesii) and blue squares (T. menziesii). ............................ 67
3-3 Results of ploidy variation in leaf cell density using, A) 1C DNA concentration following a DNA/RNA co-extraction, and B) cell counts per 2cm diameter leaf punch. ................................................................................................................. 68
3-4 Results from multiple differential expression analysis. Multi-dimensional scaling (MDS) plots A-C cluster individual based on the 500 most variable loci, with color indicating ploidal level and shape reflecting population of origin. .................................................................................................................. 69
3-5 Sum of read counts normalized per cell, and clustered by population of origin. Diploid and tetraploid mean significantly differed (p < 0.008). ................. 70
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3-6 Venn diagram contrasting the three normalization methods. Numbers within the different sections indicate loci that were identified as being differentially expressed between Tolmiea menziesii and T. diplomenziesii. ........................... 71
3-7 Loci binned by their DE categorization across the three normalization approaches. The number of loci belonging to each bin and the results of GO enrichment analyses are reported below the corresponding bin. Bins containing no loci are not shown. ....................................................................... 72
3-8 A simplified example of two observed interactions between expression level and cell density. Conservation of gene expression per biomass occurs when expression level per cell in samples with lower cell density is up-regulated enough to yield equivalent levels of transcript per unit biomass. ........................ 73
3-9 Adapted from data collected from Visger et al. 2016. Tolmiea diplomenziesii and T. menziesii did not significantly differ in photosynthetic output under common garden conditions in the greenhouses of University of Florida. ............ 74
4-1 Bar plot of the total transcriptome size per cell (the sum of per cell normalized read counts). Bar colors represent treatment day, and are grouped by individual. ......................................................................................... 95
4-2 MDA plot of the 500 most variable loci. Diploids and autotetraploids are outlined in red and green respectively, and individuals coming from different populations are distinguished by shape. Dotted lines connect each day of treatment with the corresponding individual. ...................................................... 96
4-3 Six clusters of genes exhibiting a significantly different response per transcriptome to drought over time between T. diplomenziesii and T. menziesii. Significant functional enrichments are listed beside the clusters. ..... 97
4-4 Six clusters of genes exhibiting a significantly different response per biomass to drought over time between T. diplomenziesii and T. menziesii. Significant functional enrichments are listed beside the clusters. ........................................ 98
4-5 Six clusters of genes exhibiting a significantly different response per cell to drought over time between T. diplomenziesii and T. menziesii. Significant functional enrichments are listed beside the clusters, with cluster 4 and 5 combined due to overall similarity. ...................................................................... 99
4-6 Venn diagram depicting the distribution of loci identified as responding differently to drought per transcriptome, per biomass, and per cell. ................. 100
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
THE EVOLUTIONARY IMPACT OF AUTOPOLYPLOIDY IN TOLMIEA
(SAXIFRAGACEAE)
By
Clayton J. Visger
May 2017
Chair: Douglas E. Soltis Cochair: Pamela S. Soltis Major: Botany
Polyploidy, a phenomenon involving the duplication of an organism’s
chromosome complement, is both frequent amongst Angiosperms and of major
evolutionary importance. The genus Tolmiea (Saxifragaceae) is comprised of the diploid
T. diplomenziesii and its autotetraploid derivative, T. menziesii, making it an ideal
natural system for investigating the impact of polyploidy. Using a multidisciplinary
approach I explore the ecological, physiological, and genomic divergence following
polyploidy in Tolmiea. Using an ecological modeling approach I characterize the
climatic preferences of diploid and polyploid Tolmiea, inferring that they differ most in
preferred water availability. Treating the model-based inferences as a testable
hypothesis I experimentally test the physiological response of both species to changing
soil moisture, and uncover evidence of an adaptive crossover in water-use efficiency.
To investigate the transcriptional change invoked by polyploidy I build on recent
RNAseq methodologies, developing a single assay approach to quantify transcript
abundance per transcriptome, per cell, and per unit biomass. I apply this transcript
quantification methodology towards understanding the contribution of increased gene
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dosage to differential drought response, and find T. menziesii to be more
transcriptionally active in response to drought. Taken together, the results of this
multidisciplinary study indicate that following polyploidization T. diplomenziesii and T.
menziesii have come to occupy divergent ecological niches reflecting their differences in
water-use physiology. This ecophysiological divergence correlates with major changes
in transcriptional response to drought, and is indicative of gene dosage playing a role in
the common observation that polyploids differ from their diploid progenitors with respect
to ecological niche.
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CHAPTER 1 GENERAL INTRODUCTION
Polyploidy (whole-genome duplication; WGD) has long been considered an
evolutionary dead-end by many, including some of the most prominent evolutionary
biologists of the past century (Stebbins, 1950; Wagner, 1970). However, during the last
two decades there has been a resurgence of interest, not only in the frequency of
polyploidy, but also on the impact of polyploidy from a genomic perspective (e.g., Gaeta
et al., 2007; Doyle et al., 2008; Soltis and Soltis, 2009; Gaeta and Pires, 2010; Salmon
et al., 2010; Greilhuber et al., 2012; Shi et al., 2012; Soltis et al., 2012, 2014; Madlung
and Wendel, 2013). Of the ~300,000 species of angiosperms (Christenhusz et al.,
2016), traditional estimates of the frequency of polyploidy ranged from 30-80%
(Stebbins, 1950; Lewis, 1980; Masterson, 1994). Recent genomic studies reveal,
however, that all angiosperms have experienced one or more rounds of ancient genome
duplication (Cui et al., 2006; Soltis et al., 2009; Jiao et al., 2011, 2012; Van de Peer,
2011; Van de Peer et al., 2009; Amborella Genome Project, 2014).
Despite the recent refocus on polyploidy, crucial questions remain
uninvestigated. Two types of polyploidy are generally recognized: autopolyploidy,
duplication of the same or highly similar genomes, and allopolyploidy, duplication of two
or more divergent genomes. Due in part to early and influential researchers considering
autopolyploids to be infrequent and maladaptive (Stebbins, 1950; Grant, 1981), they
have historically been understudied in all aspects relative to their allopolyploid
counterparts. Stebbins (1950), an architect of the evolutionary synthesis and a hugely
influential figure in the study of polyploidy, recognized only Galax urceolata as an
unambiguous example of autopolyploidy. As noted, the study of polyploidy has
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undergone a revival, and with that revival has come an increasing recognition of the
importance of autopolyploidy, both in its frequency and its evolutionary significance
(reviewed in Soltis and Soltis, 1993; Tate et al., 2005; Soltis et al., 2007). Autopolyploids
may be morphologically indistinguishable from or highly similar to their diploid parents
and therefore often represent cryptic species (Soltis et al., 2007). Although the ability of
autopolyploids to persist through time has been questioned (Felber, 1991), successful
autopolyploids are now considered common (Barker et al., 2016). As researchers
continue to employ high-throughput methods of assessing genome size and ploidal
level, the estimated frequency and success of autopolyploidy in nature will certainly rise.
Despite recent recognition of its frequency, we still know very little about the
ecological impact of autopolyploidy. Ecological niche modeling has been applied to a
few autopolyploid complexes. In most cases these studies have revealed that following
formation, an autopolyploid diverges from the niche space occupied by its parental
diploid (Glennon et al., 2012; McIntyre, 2012; Theodoridis et al., 2013; Marchant et al.,
2016). Whether immediate physiological effects of autopolyploidy contribute to shifts in
niche space has only been explored in a few systems (e.g., Maherali et al., 2009;
Ramsey, 2011), and in these cases the underlying genetic effects of autopolyploidy
were not explored.
Numerous recent investigations have addressed the genomic and transcriptomic
consequences of allopolyploidy (e.g., Liu et al., 2001; Chelaifa et al., 2010; Ainouche et
al., 2012; Buggs et al., 2012), but few have involved autopolyploids; of which most have
focused on synthetic autopolyploids in model systems (e.g., Arabidopsis - Li et al.,
2012) or crops (e.g., Stupar et al., 2007; Wright et al., 2009). Additionally, while both
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microarray and RNAseq data have been applied to assess global gene expression
change in several allopolyploids (e.g., Ilut et al., 2012; Combes et al., 2013; Yoo et al.,
2013; Akama et al., 2014), our understanding of autopolyploid gene expression relies
on inferences extrapolated from surveys of <20 genes (e.g., Guo et al., 1996; Yao et al.,
2011; Li et al., 2012b), a few microarray studies of crops (e.g., Stupar et al., 2007;
Muthiah et al., 2012), and RNAseq investigations into Arabidopsis (Del Pozo and
Ramirez-Parra, 2014).
Furthermore, no prior work has attempted to link the physiological and gene
expression effects of autopolyploidy with niche divergence. Hence, the ecological and
evolutionary implications of whole-genome duplication in natural autopolyploids are ripe
for study. Elucidating the consequences of genome doubling and autopolyploid
evolution requires first establishing a foundational understanding of how successful
autopolyploids diverge from their parents as well as insights into the genomic
consequences of a duplicated genome.
The angiosperm Tolmiea (Saxifragaceae) represents a potential model for the
study of natural autopolyploidy. Currently circumscribed within Tolmiea are only two
species: the autotetraploid T. menziesii (2n = 28) and its diploid progenitor T.
diplomenziesii (2n = 14) (Judd et al., 2007). Factors that commonly confound other
autopolyploid systems are lacking in Tolmiea. There is only one diploid entity, and the
close relatives of Tolmiea are highly distinct in morphology and phylogenetically (Soltis,
1984; Soltis and Kuzoff, 1995; Deng et al., 2015; Folk et al., in prep.). Hence, there are
no other closely related species that may have contributed to the formation of the
tetraploid other than T. diplomenziesii. That is, Tolmiea is an indisputable example of
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autopolyploidy in nature, which is supported by documented tetrasomic inheritance
patterns (Soltis and Soltis, 1988) and extremely high allozyme similarity between the
diploid and tetraploid (Soltis and Soltis, 1989). Although most polyploids seem to have
formed multiple times, previous research indicates a single origin of the autotetraploid T.
menziesii (Soltis et al., 1989), another advantage for a detailed comparison of a diploid
and autotetraploid. Finally, both species inhabit a range within the Pacific Northwest of
the United States with similar longitude, but different latitudes, with T. menziesii
occurring north of central Oregon and T. diplomenziesii occurring south of the tetraploid
(Soltis, 1984; Judd et al., 2007). Within most sympatric polyploid systems, gene flow
remains possible, with triploid intermediates representing the most probable genetic
bridge between cytotypes. Tolmiea menziesii and T. diplomenziesii are not sympatric,
and previous cytological studies did not find triploid intermediates, suggesting that
interspecific gene flow currently does not occur or is rare (Soltis, 1984).
For my Ph.D. at the University of Florida I elected to further develop Tolmiea as a
natural model system for the study of autopolyploidy, leveraging ecological,
physiological, and novel genomic and transcriptomic approaches to better understand
the impact of autopolyploidy within an evolutionary framework. The four main questions
that my project sought to address are: 1) did niche divergence contribute to the
observed allopatric distributions following autopolyploidization in Tolmiea?; 2) do diploid
and autotetraploid Tolmiea differ in their physiological response to stress?; 3) what are
the consequences of autopolyploidy on gene expression, and does gene function play a
role in dosage response?; and 4) when subjected to an ecologically significant stressor,
do diploid and autotetraploid Tolmiea differ in their transcriptional response? Below, I
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discuss the three research-based chapters of my dissertation and describe the roles
they play in answering the questions outlined above.
In Chapter 2 I investigate whether abiotic niche divergence has shaped the
current allopatric distribution of diploid T. diplomenziesii and its autotetraploid derivative,
T. menziesii, in the Pacific Northwest of North America (question 1) (Visger et al., 2016).
Using an integrative approach, I employed field measures of light availability, as well as
ecological niche modeling and a principal component analysis of environmental space.
This study revealed that diploid and autotetraploid Tolmiea inhabit significantly different
climatic niche spaces. The climatic niche divergence between these two species is best
explained by a shift in precipitation availability. I experimentally tested the impact of
changing water availability, finding evidence of differing physiological response to water
availability between these species (question 2).
In Chapter 3, I focus on methods for characterizing gene expression level when
transcriptome size might vary between ploidal levels. Using diploid and autotetraploid
Tolmiea as a case study, I demonstrat how spike-in RNA standards can be useful for
teasing apart shifts in total transcriptome size and cell density from sequencing depth
variation introduced while normalizing next-generation RNA sequence (RNAseq)
datasets. I build on the previously suggested use of RNA spike-ins (Lovén et al., 2012),
using them to conduct differential expression analyses across multiple biological scales,
including a novel comparison of expression level per biomass. By comparing expression
level change per transcriptome, per cell, and per biomass, I characterize a higher
fraction of the transcriptome as differentially expressed between a diploid-autopolyploid
species pair than previously reported to date (question 3).
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Chapter 4 of my dissertation ties together the insights obtained from Chapter 2,
the importance of water availability, and Chapter 3, gene expression divergence,
focusing on the transcriptional response to stress. Using the conclusions from Chapter
2 regarding the divergence in habitat water availability between diploid and tetraploid
Tolmiea, I elected to focus on the differences in transcriptional response to drought over
time. By incorporating the multiple-normalization method described in Chapter 3, I was
able to identify gene functional categories that respond to drought differentially between
the diploid and tetraploid across three biological scales. Notably, I found evidence
suggesting that the tetraploids were much more variable in their transcriptional
response relative to their diploid progenitors. Additionally, the tetraploids, which
photosynthesize more highly per cell than the diploids while well-watered, were inferred
to be disproportionally affected by drought with respect to reactive oxygen species
(ROS; a by-product of photosynthesis) accumulation in the apoplast. The tetraploids
exhibited a higher expression per biomass of multiple gene functional categories related
to ROS scavenging after 24 hours of drought stress before dropping down to diploid-like
levels. Additionally, the gene expression results suggest that in response to drought the
tetraploids dramatically reduce their tetrapyrrole production, a key component for the
synthesis of chlorophyll, and likely a key contributor to the reduction in tetraploid
photosynthesis during drought, and thereby a mechanism for restoration of redox
homeostasis.
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CHAPTER 2 NICHE DIVERGENCE BETWEEN DIPLOID AND AUTOTETRAPLOID TOLMIEA
(SAXIFRAGACEAE)1
Introduction
Estimates of the frequency of polyploidy in angiosperms have ranged from 30%
(Stebbins, 1950) to 70% (Masterson, 1994; see Grant, 1981, for range of values), with
analyses of genomic data demonstrating that all angiosperms have undergone at least
one round of whole-genome duplication (Jiao et al., 2011; Amborella Genome Project,
2014; see also Cui et al., 2006; Soltis et al., 2009; Van de Peer et al., 2009; Van de
Peer, 2011). Despite the wide recognition of the prevalence of polyploidy, both its
evolutionary role and the importance in angiosperm diversification have remained
subjects of debate (e.g., Arrigo and Barker, 2012; Mayrose et al., 2011; Scarpino et al.,
2014; Tank et al., 2015; for review see Soltis et al., 2014a). The evolutionary fate of
polyploidy rests on the assumption that, following formation, polyploids diverge from
their progenitors with respect to ecology, geography, or other factors, or a combination
of these properties (e.g., Fowler and Levin, 1984; Levin, 1983, 2002).
Two types of polyploidy have generally been recognized: autopolyploidy and
allopolyploidy (Kihara and Ono, 1926; Muntzing, 1936; Darlington, 1937; Clausen et al.,
1945; Grant, 1981). Traditionally, autopolyploidy was considered extremely rare in
nature and maladaptive (Stebbins, 1950; Grant, 1981). Stebbins (1950), for example,
recognized only one unambiguous autopolyploid, Galax urceolata. Accompanying the
recent revival of research on polyploidy has been an increasing recognition of the
frequency and importance of autopolyploidy in nature (reviewed in Soltis and Soltis,
1 This work was previously published in the American Journal of Botany. 2016 Aug;103:1396-1406. doi: 10.3732/ajb.1600130
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1993; Ramsey and Schemske, 1998, 2002; Tate et al., 2005; Soltis et al., 2007; Parisod
et al., 2010). Autopolyploids are now considered much more frequent than previously
thought, with ~25% of all investigated plant species containing multiple cytotypes (races
of different ploidy, generally presumed to be of autopolyploid origin) (e.g., Soltis et al.,
2014a; Barker et al., 2015; Rice et al., 2015).
While autopolyploidization can serve as an instant sympatric speciation
mechanism, it is also a double-edged sword, potentially yielding an isolated polyploid
individual surrounded by a population of closely related diploid organisms, subjected to
a frequency-dependent mating disadvantage (minority cytotype exclusion or MCE—
Levin, 1975; Fowler and Levin, 1984). Ramsey and Schemske (1998, 2002) estimated
the rate of unreduced gamete formation in angiosperms (~0.5–2%), concluding that
autopolyploids must form at relatively high frequency in natural populations, while also
inferring a high rate of failure to establish. To succeed as a new species, the nascent
autopolyploid must reach establishment by one of several (nonmutually exclusive)
methods: supplanting the progenitor population, achieving a chance colonization of
disturbed or recently opened habitat, or shifting its optimal niche conditions (Fowler and
Levin, 1984; Levin, 1983, 2002).
Several studies have investigated the niche differences between autopolyploids
and their diploid progenitors, with mixed results. In some cases, autopolyploidy was
accompanied or followed by niche divergence from the parental diploid (Glennon et al.,
2012; McIntyre, 2012; Theodoridis et al., 2013; Thompson et al., 2014). In contrast,
diploid and autotetraploid cytotypes of Heuchera cylindrica, for example, do not have
divergent niche requirements, despite occupying largely allopatric ranges (Godsoe et
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al., 2013). In another example, reciprocal transplanting of diploid and autotetraploid
Ranunculus adoneus revealed that niche divergence did not contribute to autotetraploid
establishment (Baack and Stanton, 2005). These seemingly contrasting results may be
explained by lineage-specific effects of polyploidy on plant physiology and abiotic stress
response. Unfortunately, however, the immediate physiological effects of polyploidy in
an ecological context remain unclear in all but a few systems (e.g., Maherali et al.,
2009; Ramsey, 2011; del Pozo and Ramirez-Parra, 2014). The effects of polyploidy are
by no means expected to be consistent across angiosperms, and therefore, the role of
stochastic establishment vs. niche divergence in the facilitation of neopolyploid
establishment needs to be evaluated on a case-by-case basis. To begin assessing the
lineage-specific effects of autopolyploidy on niche divergence, there is a need to
investigate additional autopolyploid systems, both on a broad climatic scale, and from a
more narrow experimental perspective.
The angiosperm genus Tolmiea Hook. (Saxifragaceae) is an excellent system for
the study of naturally occurring autopolyploidy. Tolmiea includes only two species,
which until recently were considered cytotypes within a single species (Soltis, 1984;
Judd et al., 2007 ). Tolmiea is now circumscribed as the autotetraploid T. menziesii
(Pursh) Torr. & A. Gray (2n = 28) and its diploid progenitor, T. diplomenziesii Judd, D.
Soltis & P. Soltis (2n = 14) (Judd et al., 2007). Both species inhabit coastal understories
of the Pacific Northwest of North America (hereafter referred to as the Pacific
Northwest), but they occupy distinct, nonoverlapping latitudinal ranges, with T. menziesii
occurring from central Oregon to south-eastern Alaska, and T. diplomenziesii from
northern California to central Oregon (Figure 2-1).
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Factors that commonly confound inference of the mode of polyploid origin are not
factors in Tolmiea. Tolmiea is morphologically, genetically, and phylogenetically distinct
from all other genera in Saxifragaceae (Soltis, 1984; Soltis and Kuzo, 1995; Soltis et al.,
1989, 2001), and it has no close relatives that may have contributed a second genome
to the formation of the tetraploid (reviewed in Judd et al., 2007). Furthermore, T.
menziesii exhibits tetrasomic inheritance (Soltis and Soltis, 1988), which is an
expectation of autotetraploids but not allotetraploids (which maintain disomic
inheritance). All of these features make T. menziesii one of the most clear-cut
autopolyploids in nature (Soltis and Soltis, 1989; Judd et al., 2007). In addition, whereas
many polyploids seem to have formed multiple times (Doyle et al., 2004; Mavrodiev et
al., 2015; Soltis and Soltis, 2009), both previous and ongoing research, including
restriction-site analyses (Soltis et al., 1989) and phylogenetic analyses of plastid
sequences, ITS sequences, and transcriptome data (C. Visger, University of Florida,
unpublished data), indicates fewer origins of T. menziesii than other well-documented
autopolyploids (e.g., Servick et al., 2015); it has formed once or perhaps only a few
times during a limited time frame.
Here, we investigate the factors contributing to the nonoverlapping geographic
ranges of T. menziesii and T. diplomenziesii. Specifically, we ask whether their spatial
separation can be explained by climatic niche divergence, rather than chance
geographical isolation. In a review spanning both auto- and allopolyploids, Glennon et
al. (2014) found that polyploid species most o en occupied niche spaces that were a
subset of their progenitors’ niches, indicating that ecological novelty might not be the
most common mode of establishment. Rather, as observed between diploid and
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autotetraploid cytotypes of Heuchera cylindrica (Godsoe et al., 2013) and Ranunculus
adoneus (Baack and Stanton, 2005), spatial separation unaccompanied by niche
divergence can serve as a mechanism for escape from minority cytotype disadvantage.
Although allopolyploids may exhibit a range of niche spaces relative to those of their
parents (Marchant et al., 2016), given the results for Heuchera cylindrica (a species
closely related to Tolmiea; Soltis et al., 2001) (Godsoe et al., 2013) and the
autopolyploid nature of T. menziesii, niche equivalency (conservatism) between T.
diplomenziesii and T. menziesii is our null expectation, with subsequent divergence
occurring only in spatial distribution.
To address whether spatial separation between T. menziesii and T.
diplomenziesii is accompanied by abiotic niche divergence, we integrated comparisons
of climatic niche, field-based measurements, and physiology within a common garden.
We applied niche modeling to estimate the climatic niche spaces of T. menziesii and T.
diplomenziesii, followed by an ordination-based analysis of climatic variables to provide
an independent assessment of shifts in environmental space between T. diplomenziesii
and T. menziesii. Both species are shade-loving understory plants, therefore, we used
measures of canopy transmittance to identify whether divergence in shade preference
has occurred. Finally, we integrated physiological comparisons with our investigation of
climatic niche divergence by simulating drought conditions in a common garden to
experimentally test the hypothesis that T. menziesii and T. diplomenziesii differ in
response to drought stress.
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Materials and Methods
Sampling
Thirty-one populations of Tolmiea were sampled across northern California and
the Pacific Northwest in June 2013. Leaf tissue was collected in silica desiccant from
multiple individuals spaced at least 5 m apart to minimize the possibility of resampling
clonal genotypes. Two live plants per population were transported to the greenhouses
at the University of Florida, where they were clonally propagated and raised in a
common garden. Additionally, two voucher specimens were collected from each
population and deposited in the California State University, Sacramento Herbarium
(SACT) (Table 1 for voucher information).
Flow cytometry
Flow cytometry was used to infer the ploidy of two individuals per population.
Given the nonoverlapping ranges of T. diplomenziesii and T. menziesii, as well as
previously published evidence supporting the absence of mixed-ploidy populations
(Soltis, 1984; Soltis and Soltis, 1989), this sampling strategy was considered sufficient
to estimate the ploidy of each population. Flow cytometry methods were modified from
Hanson et al. (2005). Approximately 2.5 cm2 of silica-dried leaf tissue were suspended
in 500 μL of ice-cold extraction buffer (0.1 M citric acid, 0.5% v/v Tri- ton X-100, 1% w/v
PVP-40) and co-chopped with 1 cm2 of standard [Pisum sativum ‘citrad’; 9.09 pg
(Doležel et al., 1998)] in a plastic petri dish over a chilled brick with a fresh razor blade.
Tissue was chopped ~45–60 s, until it resulted in a fine slurry. The resulting slurry was
swirled by hand until the extraction buffer obtained a light-green tinge. A 100-μm mesh
filter (BD Falcon; Becton, Dickinson and Company, Franklin Lakes, NJ, USA) was used
to strain the solution. After straining, 140 μL of filtrate were treated with 1 μL of RNaseA
24
(1 mg/mL) and 350 μL of a propidium iodide staining solution (0.4 M NaPO4, 10 mM
sodium citrate, 25 mM sodium sulphate, 50 μg/mL propidium iodide) and incubated on
ice for approximately 3 h. An Acuri C6 (BD Biosciences, USA) was used to analyze the
stained solutions until 10,000 events were captured, and genome size and ploidy were
inferred relative to the internal standard.
Ecological niche modeling (ENM)
Occurrence data for both Tolmiea species (359 points) were mined from the
Consortium of Pacific Northwest Herbaria (http://www.pnwherbaria.org) and added to
the localities of the 31 field-sampled populations. Given the recent description of diploid
Tolmiea as T. diplomenziesii (Judd et al., 2007), many diploid specimens in herbaria are
still referred to as T. menziesii because of insufficient time for annotation of specimens.
Therefore, herbarium data from central Oregon (where ranges of T. diplomenziesii and
T. menziesii abut) were excluded: only populations from this region for which ploidy was
verified via flow cytometry were used (see Figure 2-1). Duplicate specimen records
were removed from the data set. Additionally, occurrence data were examined visually,
and any cases of clear misidentification were removed (e.g., an occurrence was
reported in eastern Oregon where Tolmiea does not occur). In total, 310 data points
were used, 72 for T. diplomenziesii and 207 for T. menziesii.
Bioclimatic and elevation layers were obtained from Worldclim (Hijmans et al.,
2005) at 30 arcsec resolution. In some cases, information on soil type can provide
valuable information for ENM; however, for this study we found the resolution of
publically available soil layers to be too coarse-grained to be informative at the
geographic scale investigated here. Additionally, the ability of both T. diplomenziesii and
25
T. menziesii to thrive on both rich soil and bare, rocky seeps suggests that soil type is
not likely to be a major determinant of habitat suitability. We therefore opted to
investigate only the climatic and elevation layer sets.
Elevation and all 19 Bioclim layers were tested for correlation within the Pacific
Northwest, and a cutoff of 0.7 Pearson’s correlation coefficient was imposed to reduce
the number of layers for subsequent analyses. Eight layers were retained for the final
analysis: Bio2 (mean diurnal range), Bio5 (max temperature of warmest month), Bio8
(mean temperature of wettest quarter), Bio11 (mean temperature of coldest quarter),
Bio15 (precipitation of driest month), Bio16 (precipitation of seasonality), Bio17
(precipitation of driest quarter), and Bio18 (precipitation of warmest quarter).
Niche models were generated using the logistic output from MaxEnt (ver. 3.3.3k;
Phillips et al., 2004, 2006). With the exception of using 15 subsampled replicates, a
25% random test percentage, and 5000 maximum iterations, default settings were used.
The model accuracy was evaluated by the area under the curve (AUC) statistic, which
reflects the ability of the model to correctly predict the occurrence of training points, as
well as visual comparison of the training and testing output curves and our expert
knowledge on the habitat map.
Niche overlap
Overlap between the niche space of T. diplomenziesii and T. menziesii was
summarized using Schoener’s D from 0 (no similarity) to 1 (complete similarity) (Warren
et al., 2008; Broennimann et al., 2012). We then compared our observed niche overlap
against null distributions of niche overlap scores generated under both the assumptions
of niche equivalency and similarity within environmental space (following Broennimann
et al., 2012) using an ordination-based approach shown to be less prone to biases
26
associated with model-based tests of niche divergence (see Glennon et al., 2014).
Background environmental space was defined using climate data extracted from 10,000
random points (both 500 and 1000 background points were also used and did not yield
differing results) across the realized range of T. menziesii and T. diplomenziesii using
the QGIS point sampling tool, and the similarity and equivalency tests were
implemented in the R package ecospat (Broennimann et al., 2014) with 100 replicates
each.
Environmental principal component analysis (PCA)
Climatic values from each minimally correlated climatic layer were extracted for
each occurrence point. The resulting matrix was then transformed into principal
components using JMP pro (version 12; SAS Institute, Cary, NC, USA), and visual
inspection of a scree plot was used to assess the number of components to retain.
Using R (R Development Core Team, 2015), occurrence points were plotted against the
optimal number of principal components best explaining the data set.
Physiological response to changes in soil moisture
Twelve clonally propagated plantlets (six T. diplomenziesii and six T. menziesii)
were grown in a soil mix containing 1/8 sand, 1/8 fine gravel, and 3/4 Professional
Growing Mix (Sun Gro Horticulture, Agawam, MA, USA) under common conditions in
the greenhouses at the University of Florida. Over a 17-d period, watering was ceased,
and soil moisture (percent volumetric water content), leaf water potential (ΨLEAF
), and
intrinsic water-use efficiency (WUE—the ratio between CO2 assimilation (A) and
stomatal conductance (gs)) were measured on five separate days (1, 3, 10, 15, and 17 d
after water cessation) using a soil moisture meter (10HS; Decagon Devices, Pullman,
27
WA, USA), Scholander pressure chamber (Model 600D; PMS Instrument Company,
Albay, OR, USA), and an open gas-exchange system (Li-Cor 6400; Li-Cor, Lincoln, NE,
USA), respectively. Additionally, leaf punches of standardized area were collected from
each individual plant on days 1, 3, 10, 15, and 17 post watering, and weighed. The leaf
punches were then dried at 70°C overnight and reweighed for dry weight. Leaf water
content was calculated on a fresh weight basis using the formula (fresh weight–dry
weight)/fresh weight × 100. Each data set was checked for normality and log
transformed if it significantly deviated from a normal distribution. A linear mixed-effect
model was implemented in JMP pro to assess the effects of ploidy, soil moisture, and
ploidy by soil moisture, with individual as a random effect, on leaf water potential, leaf
water content, and WUE.
Guard cell measurements
The abaxial leaf surface was painted with a thin layer of clear nail polish and
allowed to dry, and the nail polish was carefully peeled away from the leaf, yielding a
negative epidermal impression. These negative epidermal impressions were measured
using a phase microscope under a 40× objective and photographed using a Nikon
Coolpix 950. Image analysis was conducted using Fiji (Schindelin et al., 2012), and
length/width of guard cells was recorded using pixels as the unit of measure, allowing
for comparisons of relative size differences. The effect of ploidy on length, width, and
area of guard cells was tested using a t test implemented in JMP pro.
Canopy transmittance
The percentage of total light transmitted through the canopy was assessed using
methods modified from Sessa and Givnish (2014). Digital hemispherical photographs
were taken above a plant, with five plants per population across 31 populations using a
28
camera with a fisheye lens attachment (Nikon Coolpix 950 with FC-E8 fisheye). Data for
five populations were removed because of equipment failure (see Table 2-1). Given the
potential for clonality in Tolmiea, we scattered our data collection locations within each
population, with no image being taken within 10 m of an already photographed location.
The camera was oriented due North using a compass and leveled with a bubble level.
Images were analyzed using the Gap Light Analyzer (GLA) software (Frazer et al.,
1999), and saturation levels were adjusted by eye by a single researcher (CJV). A linear
mixed-effect model was implemented in JMP pro 12 to assess the effects of ploidy and
elevation, with population as a random effect, on the per-population average canopy
transmittance.
Results
Flow cytometry
Ploidy estimates using flow cytometry readily distinguished T. diplomenziesii and
T. menziesii (populations for which ploidy was verified using FCM are identified using
stars in Figure 2-1). Populations south of central Oregon were confirmed to be diploids
(2n = 2x = 14), and those samples north of central Oregon were tetraploid (2n = 4x =
28). No triploid individuals were found using this approach, supporting the findings of
Soltis (1984) and Soltis and Soltis (1989) regarding the rarity or nonexistence of triploid
Tolmiea in nature.
ENM and niche overlap
Over 15 replicate runs, the niche models generated for T. diplomenziesii and T.
menziesii yielded mean ± SD. AUC scores of 0.962 ± 0.015 and 0.929 ± 0.012, implying
very low rates of false negative and false positive suitability predictions (for response
curves see Figure A-1). The climatic niche spaces of both species closely mirror their
29
realized ranges, with the climatic niche space of T. diplomenziesii slightly expanded
northward beyond the realized range (Figure 2-2). The niche overlap of T.
diplomenziesii and T. menziesii within environmental space (Schoener’s D = 0.140;
Figure 2-3 dashed line) was significantly lower than the null distribution under niche
equivalency (Figure 2-3A solid histogram; p = 0.0198). Using the niche similarity test,
we found that T. diplomenziesii and T. menziesii are neither more nor less similar than
expected by chance within environmental space (T. diplomenziesii vs. T. menziesii, and
T. menziesii vs. T. diplomenziesii, p = 0.267 and p = 0.376, Figure 2-3B and Figure 2-
3C, respectively; for the related PCA plots, see Figure B-1). We therefore reject the null
hypothesis that T. menziesii inhabits a climatic niche space equivalent to T.
diplomenziesii, while acknowledging that the climatic regimes they occupy may be only
subtly divergent.
Environmental PCA
Two principal components explain the majority of the environmental variation
across the distribution of the entire Tolmiea data set (Figure 2-4), with an eigenvalue of
3.1368, which dropped dramatically when a third component was included (eigenvalue
= 0.6402). Principal component 1 (PC1) represented 43.6% of the variation and was
most explained by mean temperature of the wettest quarter (eigenvector 0.507) and
mean temperature of the coldest quarter (eigenvector 0.501). Principal component 2
(PC2) represented 39.2% of the variation and was most explained by precipitation of the
driest quarter (eigenvector 0.518) and precipitation of the warmest quarter (eigenvector
0.534). Neither of the major contributing variables of PC1 was strongly correlated with
any precipitation variables removed prior to model generation, and neither of the PC2
major contributors was correlated strongly with removed temperature variables. The
30
PCA reveals a large overlap in environmental space between T. diplomenziesii and T.
menziesii along the temperature-related axis (PC1), with the tetraploid showing greater
breadth. However, along the precipitation-related axis (PC2), there is very little overlap
between the two species, with T. diplomenziesii inhabiting drier conditions than T.
menziesii (Figure 2-4).
Physiological response to changes in soil moisture
Soil moisture, leaf water content, and leaf water potential collected over the
course of the 17-d dry down in the common garden deviated from normal distributions
and were log transformed prior to statistical analysis. Leaf water content was not
significantly affected by any of the investigated variables (ploidy, soil moisture, and
ploidy by soil moisture); ploidy by soil moisture was nearly significant with p = 0.07). Soil
moisture had a significant effect on leaf water potential (p < 0.0001) (Table 2-2, Figure
2-5). Conversely, WUE was significantly affected by soil moisture (p = 0.0372) and the
ploidy by soil moisture interaction (p = 0.0368) (Table 2-2, Figure 2-5).
Guard cell measurements
Guard cell dimensions did not differ between T. diplomenziesii and T. menziesii (t
test; Table 2-3). Mean ±SD guard cell length, width, and area for diploids were 135.7 ±
19.0 pixels, 108.0 ± 12.6 pixels, and 14,808.7 ± 3601.3 pixels2, whereas corresponding
guard cell dimensions for tetraploids were 130.7 ± 19.6 pixels, 104.5 ± 13.5 pixels, and
13,802.0 ± 3500.7 pixels2.
Canopy transmittance
Canopy transmittance across the data set showed high levels of variation within
and between populations of both species, overwhelming any small differences that
might be present between species (Table 2-1). The range of canopy transmittances for
31
diploid populations was 5.2–22.4% (mean ± SD = 9.9 ± 3.9%), and was very similar to
the range for the tetraploid populations of 5.1–25.9 (mean ± SD = 10.8 ± 3.7%). Neither
ploidy alone (p = 0.2056) nor ploidy by elevation (p = 0.9023) had a significant effect on
canopy transmittance, although increasing elevation did have a significant positive
effect on canopy transmittance (p = 0.0242) (Table 2-2).
Discussion
Autotetraploids in general tend to represent a discrete subset of the genetic
variation encompassed by all of the populations of their diploid progenitor, which,
barring any polyploidy-derived changes, should translate initially to a conserved
physiology and niche preference. Using a novel integration of niche modeling, field
measurements, and physiological investigations, we found evidence for both abiotic
niche differentiation and corresponding physiological divergence between a diploid
parent and its autopolyploid derivative.
Tolmiea diplomenziesii and T. menziesii are highly similar in morphology,
flavonoids (Soltis and Bohm, 1986), allozymes (Soltis and Soltis, 1989), rDNA restriction
sites (Soltis and Doyle, 1987), and ITS sequences (C. J. Visger, University of Florida,
unpublished data). Despite their similar morphology and genetics, we found evidence
that T. menziesii inhabits a unique climatic niche relative to that of its diploid progenitor.
One possible explanation for the niche divergence of T. menziesii compared to T.
diplomenziesii is that despite very high genetic similarity, T. menziesii may exhibit
transgressive levels of gene expression as a consequence of carrying four alleles per
locus. However, whether climatic niche divergence between the diploid and
autotetraploid was an immediate effect of polyploidy per se, a product of subsequent
evolution, or a combination of both is beyond the scope of this study and is under
32
investigation. Therefore, it is important to consider our findings as representing niche
divergence following a combination of both autopolyploidy and subsequent evolution.
Selection on nascent polyploids should favor factors that facilitate escape from
MCE. That is, traits that encourage a shift in geographic space or niche space should
be subjected to strong positive selection (Levin, 1975; Fowler and Levin, 1984). While
some autopolyploids (e.g., Heuchera cylindrica; Godsoe et al., 2013) appear to have
shifted from their diploid progenitor in geography alone, it appears that T. menziesii and
T. diplomenziesii have diverged in geography, climatic regime, and abiotic preference.
The climatic niche spaces of T. menziesii and T. diplomenziesii appear to be
predominantly non-overlapping geographically (Figure 2-2), suggesting that niche
divergence has played a role in their allopatric distribution. We rejected the null
hypothesis of a highly conserved T. menziesii niche, finding instead that the two species
inhabit divergent climatic regimes. The null model of niche equivalency can potentially
be biased by geographic autocorrelation (Warren et al., 2008), and in some cases may
only indicate divergent climatic regimes. To compensate for any potential biases and/or
oversensitivity of the equivalency test, we conducted the less stringent test of niche
similarity and were unable to find support for either more or less similar niche space
between T. diplomenziesii and T. menziesii. The niche identity/equivalency test is an
extremely sensitive test, and sister taxa will most often reject the null expectation of
niche equivalence (e.g., Kalkvik et al., 2012). The similarity test is far less stringent and
rarely finds sister species to be significantly dissimilar, reflecting the tendency for the
niches of closely related species to be more similar than expected by chance (e.g.,
Burns and Strauss, 2011). However, Warren et al. (2008) argued that investigation of
33
niche conservatism should be treated as a continuum, with most sister taxa falling
somewhere between equivalent and dissimilar. We stress that for autopolyploidy, which
provides no unique alleles and often results in morphologically identical progenitor-
derivative pairs, the null expectation should fall closer to equivalence than dissimilarity.
Even a slight deviation from niche equivalency following autopolyploidization may be
important for escape from MCE.
Our modeling and environmental PCA indicates that niche divergence in Tolmiea
occurs largely along an axis of precipitation, with T. diplomenziesii inhabiting regions
with lower precipitation and T. menziesii occurring under high-precipitation regimes. Our
experiment testing response to reduced water availability supports this conclusion.
There appears to be a trade-off, or adaptive crossover, at high and low soil moisture
between the two species, with T. menziesii photosynthesizing more efficiently with
respect to water loss through transpiration during times of high soil moisture, while T.
diplomenziesii makes more efficient use of water under drought conditions. Significantly,
leaf water potential did not appear to be affected by either ploidy or its interaction with
soil moisture, indicating that autopolyploidy and subsequent evolution have not altered
the regulation of leaf water potential.
Guard cell size has been repeatedly shown to be a strong correlate of genome
size (and therefore ploidy) (e.g., Beaulieu et al., 2008; Masterson, 1994). Changes in
guard cell size could alter the stomatal aperture, thereby influencing water dynamics
(Mishra, 1997; Li et al., 1996). Interestingly, we find no evidence for a change in guard
cell length, width, or area between T. diplomenziesii and T. menziesii. Although there
are a number of examples where polyploidy does increase guard cell size, this is not
34
always the case (e.g., Mishra, 1997; Masterson, 1994). Had significant differences been
found between guard cells of T. diplomenziesii and T. menziesii, this would have
provided a clear explanation for the differential response to water availability observed.
A relationship between ploidy and stress tolerance, in particular drought
tolerance, has been observed (and in some cases empirically studied) in a number of
plant systems (e.g., Li et al., 1996; Hao et al., 2013; del Pozo and Ramirez-Parra,
2014). Often the higher ploidy has been found to be more suited to dry environments.
This trend has usually been attributed to the common observation that polyploids have
larger stomatal apertures but reduced stomatal densities, which presumably results in
altered transpiration rates (a component of WUE; see te Beest et al., 2012). In Tolmiea,
we find the opposite of these trends. Our study revealed that the diploid, and not the
autotetraploid, is more suited to drier habitats. One possible explanation is that unlike
most polyploids, T. menziesii does not have significantly enlarged guard cells. This
observation supports the idea that alteration of stomata size and density may be
important components of the polyploidy-induced drought tolerance that has commonly
been observed. While we did not investigate the cell size of non-stomatal cells (e.g.,
pavement cells, mesophyll cells, vessel elements), other cell size effects could certainly
play a role in the divergent WUE we observed. Ploidy has been shown to influence
xylem diameter (Pockman and Sperry, 1997), which could influence the relationship
between leaf water potential and leaf water content by requiring increased turgor
pressure to maintain a given leaf water content. In the context of our results (no
significant effect of ploidy on leaf water potential and a nearly significant effect of ploidy
on leaf water content at p = 0.07), differing xylem diameter could potentially explain the
35
maintenance of similar leaf water potential under differing leaf water contents; how-
ever, this was beyond the scope of our study, but warrants further investigation.
Although we have shown evidence for climatic divergence with respect to water
availability and a differing response to drought between T. diplomenziesii and T.
menziesii, based on our measures of canopy transmittance, we found no such
differences relating to shade preference. Conservation of shade preference between
ploidal levels makes sense in light of the geographic distribution of the species. Had T.
diplomenziesii and T. menziesii diverged primarily in shade preference, we would not
expect the observed north–south allopatric distribution; rather, we might expect a more
patchy or mosaic-like distribution varying with canopy type of differing canopy
transmittance (e.g., evergreen vs. deciduous forest, mature vs. new growth forest).
However, we found that no individuals occurred under greater than 26% canopy
transmittance, suggesting that the occurrence of both species are similarly constrained
by shade availability. The ecological and physiological divergence between an
autopolyploid and its diploid progenitor may be explained by several factors. While
allopolyploid genomes may undergo fractionation (e.g., Langham et al., 2004), reducing
redundant gene copies, autotetraploids should persist with four allelic copies through
time, assuming disomic inheritance is not restored. Increased allelic dosage has been
shown to affect plant physiologic stress response, including drought stress (del Pozo
and Ramirez-Parra, 2014). Maintaining four allelic copies also has a significant
population genetic impact in the fixing/purging of beneficial/deleterious alleles, slower
rates of reaching Hardy-Weinberg equilibrium, and higher heterozygosity (Moody et al.,
1993). Even if polyploidy per se has had no immediate effect on the ploidy differences
36
described here in Tolmiea, the increase in allele copy number could have major effects
on the subsequent evolution of autopolyploid populations. Furthermore, increases in cell
volume following polyploidization are typically nonlinear (e.g., 1.5-fold cell volume vs. 2-
fold nuclear material), which could influence intracellular mechanisms under
concentration-dependent control (Levin, 1983; Storchova et al., 2006). Although guard
cell sizes did not differ, it remains possible that the size of other cell types may differ
between T. diplomenziesii and T. menziesii. A combination of nucleotypic effects and
changes in gene dosage following polyploidy could underlie altered water use and/or a
shift in abiotic requirements (reviewed in Soltis et al., 2014b) and are currently being
explored in Tolmiea.
Conclusion
To determine the role of polyploidy in features that might lead to niche
divergence, additional studies leveraging resynthesized autopolyploids are necessary
(e.g., Ramsey, 2011). In some cases, autopolyploidy has resulted in instantaneous
physiological divergence, including differences in water-use efficiency (Ramsey, 2011;
del Pozo and Ramirez-Parra, 2014), while in other cases, polyploidy may merely
provide the genetic substrate for subsequent evolution (Levin, 1983). Following
autopolyploidization, a presumed combination of polyploidy and subsequent
evolutionary pressures drove T. diplomenziesii and T. menziesii to diverge in climatic
niche preference. We therefore suggest caution when interpreting results such as these
relative to the role of polyploidy per se. Our data provide a convincing example of niche
differentiation between a diploid and its autotetraploid derivative, as well as strong
rationale for subsequent studies to tease apart the relative effects of autopolyploidy and
subsequent evolution.
37
Table 2-1. Population locality, ploidal level, elevation, and canopy transmittance of diploid and tetraploid Tolmiea. Population vouchers are housed in the California State University, Sacramento herbarium (SACT).
Percent Canopy Openness
Population SACT herbarium ID Ploidy Latitude Longitude Elevation (ft) Measure
1 Measure
2 Measure
3 Measure
4 Measure
5
13-1 C. Visger 13-1 2x
40.824417° -123.691833° 1175.61 14.60 21.76 15.56 11.15 11.46
13-2 C. Visger 13-2 2x
40.523617° -122.940733° 761.39 9.96 7.26 10.64 7.67 9.53
13-3 C. Visger 13-3 2x
40.374067° -123.363717° 843.99 16.19 6.11 22.37 20.44 5.97
13-4 C. Visger 13-4 2x
41.679950° -123.568150° 1356.06 na na na na na
13-5 C. Visger 13-5 2x
41.335383° -123.395200° 311.20 na na na na na
13-6 C. Visger 13-6 2x
42.111683° -123.409983° 1020.47 9.79 8.37 7.92 7.73 6.96
13-7 C. Visger 13-7 2x
43.297050° -122.908983° 269.44 14.61 5.22 6.93 6.79 na
13-8 C. Visger 13-8 2x
43.290883° -122.564800° 471.83 9.28 6.10 6.97 7.15 8.98
13-9 C. Visger 13-9 2x
43.786433° -122.549817° 388.32 7.38 8.58 7.43 6.26 7.42
13-10 C. Visger 13-10 2x
43.810600° -122.424967° 416.97 10.09 13.65 10.06 10.28 10.12
13-11 C. Visger 13-11 4x
44.070200° -122.229650° 610.51 12.41 12.26 12.56 14.85 15.79
13-12 C. Visger 13-12 4x
44.403517° -122.086183° 1053.69 25.94 22.82 10.12 7.52 12.73
13-13 C. Visger 13-13 4x
44.399867° -122.340600° 395.02 7.22 9.85 15.54 12.36 7.87
13-14 C. Visger 13-14 4x
44.602050° -121.962800° 799.49 14.02 11.08 11.13 12.01 12.37
13-15 C. Visger 13-15 4x
44.762900° -122.111133° 574.55 11.93 10.63 10.27 9.05 6.01
13-16 C. Visger 13-16 4x
45.025383° -121.955750° 630.33 12.28 9.39 10.61 10.18 8.56
38
Table 2-1. Continued Percent Canopy Openness
Population SACT herbarium ID Ploidy Latitude Longitude Elevation (ft) Measure
1 Measure
2 Measure
3 Measure
4 Measure
5
13-17 C. Visger 13-17 4x
45.043333° -122.062633° 463.30 8.13 13.93 10.55 7.30 6.30
13-18 C. Visger 13-18 4x
45.303167° -121.867700° 666.29 9.32 7.93 7.34 10.02 11.27
13-19 C. Visger 13-19 4x
45.816683° -121.881433° 307.85 14.41 12.45 12.78 17.16 14.99
13-20 C. Visger 13-20 4x
46.053283° -121.971183° 389.53 8.79 6.16 5.08 5.90 5.19
13-21 C. Visger 13-21 4x
46.634950° -121.710300° 499.87 9.86 9.13 11.09 8.84 9.81
13-22 C. Visger 13-22 4x
47.158000° -121.726733° 476.71 13.08 11.44 10.74 14.10 9.62
13-23 C. Visger 13-23 4x
47.403150° -121.567733° 451.71 8.38 7.70 8.03 7.93 10.05
13-24 C. Visger 13-24 4x
47.727917° -121.406900° 298.70 11.66 9.27 8.93 7.79 8.23
13-25 C. Visger 13-25 4x
48.369033° -121.504750° 224.03 10.25 9.93 11.30 10.92 12.84
13-26 C. Visger 13-26 4x
48.902100° -121.913300° 336.80 6.01 14.75 15.79 12.32 9.51
13-27 C. Visger 13-27 4x
47.790467° -122.925450° 138.07 9.42 9.48 8.98 7.77 8.75
13-28 C. Visger 13-28 4x
45.180033° -123.817933° 70.10 16.72 11.07 11.32 10.65 9.59
13-29 C. Visger 13-29 2x
44.397867° -123.860467° 26.82 8.79 6.63 9.86 9.08 8.90
13-30 C. Visger 13-30 2x
42.825633° -124.008150° 173.74 12.13 8.89 8.17 9.67 9.55
13-31 C. Visger 13-31 2x
42.118750° -124.195900° 35.97 na na na na na
39
Table 2-2. Linear mixed-effects model results for canopy transmittance and physiological response.
Response variable Effect F ratio
Prob > F
log Leaf Water Potential Ploidy 0.13 0.7311 log Leaf Water Potential log(Soil Moisture) 24.59 <0.0001 log Leaf Water Potential Ploidy*log(Soil Moisture) 3.13 0.0864 Water-use Efficiency Ploidy 0.64 0.4450 Water-use Efficiency log(Soil Moisture) 4.56 0.0372 Water-use Efficiency Ploidy*log(Soil Moisture) 4.58 0.0368
log Leaf Water Content Ploidy 2.09 0.1785 log Leaf Water Content log(Soil Moisture) 0.19 0.6651
log Leaf Water Content Ploidy*log(Soil Moisture) 3.40 0.0704 Canopy Transmittance Ploidy 1.69 0.2056 Canopy Transmittance Elevation 5.79 0.0242 Canopy Transmittance Ploidy*Elevation 0.02 0.9023
40
Table 2-3. Guard cell size vs ploidy
Measurement t Ratio Prob > |t|
Guard cell length 4x vs 2x -1.01055 0.3176 Guard cell width 4x vs 2x -1.06126 0.2940 Stomatal area 4x vs 2x -1.10074 0.2771
41
Figure 2-1. Sampling map of the Pacific Northwest. All data points, excluding those in green, were used for niche model generation. Circles represent data mined from herbaria, while stars represent field-collected accessions with ploidy verified by flow cytometry (FCM).
42
Figure 2-2. Niche suitability across the Pacific Northwest for T. diplomenziesii (blue) and
T. menziesii (red) categorized by climatic suitability score as low (0.15–0.3), moderate (0.3–0.5), and high (> 0.5). The dashed line indicates the realized North/South range break for T. diplomenziesii and T. menziesii.
Tolmiea menziesii predicted niche suitability
Tolmiea diplomenziesii predicted niche suitability
Low
Moderate
High
Low
Moderate
High
43
Figure 2-3. Observed niche overlap (red dashed line – 0.140) vs. a distribution of 100 niche overlap scores generated under the null assumption of niche equivalency (A) and similarity (T. diplomenziesii vs. T. menziesii, and T. menziesii vs. T. diplomenziesii, B and C, respectively).
0
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44
Figure 2-4. Tolmiea diplomenziesii (blue) and T. menziesii (red) populations plotted against temperature-related variables (PC1) and precipitation-related variables (PC2). Ellipses represent 90% confidence.
-6 -4 -2 0 2 4
-8-6
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Principal component 1 (43.6% - Temperature-related variables)
Princip
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ita
tion-r
ela
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s)
Tolmiea diplomenziesiiTolmiea menziesii
45
Figure 2-5. Log soil moisture vs. intrinsic water-use efficiency (top), log leaf water
content (middle), and log leaf water potential (bottom). Regression lines denote a significant effect on the y-axis due to log soil moisture (gray) or ploidy by soil moisture interaction (colored by species).
20
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Intr
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log Soil moisture (percent volumetric water content)
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46
CHAPTER 3 DIVERGENT GENE EXPRESSION LEVELS BETWEEN DIPLOID AND
AUTOTETRAPLOID TOLMIEA RELATIVE TO THE TOTAL TRANSCRIPTOME, THE CELL, AND BIOMASS
Introduction
Polyploidy (whole-genome duplication; WGD), a process now recognized to be of
major importance across many eukaryotic lineages (Van de Peer et al., 2009; Jiao et
al., 2011; Van de Peer, 2011; Jiao and Paterson, 2014), was long considered an
evolutionary dead-end by some, including several of the most prominent evolutionary
biologists of the past century (Stebbins, 1950; Wagner, 1970). However, during the last
several decades there has been a resurgence of interest, not only in the frequency of
polyploidy (Barker et al., 2015), but also in the genetic and genomic consequences of
polyploidy (e.g., Barker et al. 2016; Gaeta et al., 2007; Doyle et al., 2008; Soltis and
Soltis, 2009, 2012; Salmon et al., 2010; Greilhuber et al., 2012; Shi et al., 2012;
Madlung and Wendel, 2013; Soltis et al., 2014).
The role of polyploidy in facilitating changes in gene expression, through
expression level divergence, altered expression patterns (e.g., across tissue-types),
and/or through the generation of unique splice variants is arguably one of the most
important research topics in the field today (e.g., Liu et al., 2001; Adams et al., 2003;
Chelaifa et al., 2010; Dong and Adams, 2011; Ainouche et al., 2012; Buggs, 2012;
Rambani et al., 2014 -- see Yoo et al., 2014 for review). However, the rapid increase in
studies of patterns of gene expression in polyploids may have outpaced our
fundamental understanding of the transcriptome’s response to polyploidy per se.
47
One major concern in comparing gene expression between diploids and
polyploids is that any transcriptome amplification or transcriptome-wide effects induced
following polyploidy are rarely, if ever, investigated (see Coate and Doyle, 2010, 2015).
Transcriptional amplification is a biological phenomenon where the total mRNA
produced per cell is increased up to several fold in one treatment group compared to
another, and results in unequal total transcriptome sizes (Nie et al., 2012).
Transcriptional amplification would be anticipated in a polyploid compared to a diploid
progenitor, as polyploidy globally alters gene/genome copy number and often influences
cell size; the latter change has been shown to strongly correlate with transcriptome size
(Fomina-yadlin et al., 2014).
Surprisingly, transcriptome size variation has been taken into account in a cross
ploidy comparison of gene expression divergence in only one study (Coate and Doyle,
2010). Coate and Doyle (2010) found allotetraploid G. dolicocarpa to have a total mRNA
transcriptome ~1.4 times greater than its diploid progenitors, suggesting that polyploidy
can induce a transcriptome size increase. However, without data from other polyploid
systems, the prevalence of this phenomenon remains unclear.
Recent work has shed light on some of the biases inherent to expression level
comparisons between treatment groups that differ in transcriptome size. Loven et al.
(2012) showed that inferences drawn from a typical RNAseq workflow can be
confounded when treatment groups have transcriptomes of differing size. Because
transcriptome size variation has rarely been explored, RNAseq studies involving
transcriptome amplification are likely to underestimate the proportion of the
transcriptome being differentially expressed.
48
Modern methods for comparing gene expression level change across the
transcriptome rely on the detection of statistically different RNAseq read abundances at
each locus-- a differential expression analysis. RNAseq libraries are usually sequenced
to different depths by chance, resulting in library size (the total number of reads per
sample) varying across the final dataset. To prevent variation in library size from
influencing differential expression analyses, library size is typically normalized across all
samples (e.g., Mortazavi et al., 2008). Commonly used library normalization methods
quantify expression level on a concentration basis by dividing read abundance by a
factor of the whole library (e.g., transcripts per million or reads per kilobase per million)
(see Coate and Doyle, 2010, 2015; Lovén et al., 2012). Concentration-based
normalizations place gene expression in the context of expression level per
transcriptome. Using a concentration-based normalization approach to infer changes in
expression level requires that total transcriptome size does not vary between ploidal
levels. If transcriptome size varies by ploidy, then loci identified as differentially
expressed (differentially expressed genes; DEGs) are not necessarily expressed at
different levels, rather they are maintained in different concentrations relative to the
transcriptome (Figure 1A). To overcome these problems, the use of synthetic spike-in
RNA standards has been proposed (Lovén et al., 2012). These standards developed
by the External RNA Controls Consortium (Baker et al., 2005; External RNA Controls
Consortium, 2005)(Spike-in RNA standards; henceforth spike-ins) facilitate comparison
of absolute expression level across treatments with different transcriptomes sizes (see
Scott Pine et al., 2016). Spike-ins allow for normalization of transcript abundance
49
independent of transcriptome size, and therefore represent a promising method for
cross ploidy comparisons.
Most polyploids exhibit cell size changes relative to their parents, which may
result in an alteration of cell density (see Stebbins, 1971; Masterson, 1994). As
discussed above, per transcriptome normalized comparisons are concentration based,
and are therefore robust to variations in cell size and density between treatments. Cell
size and density are rarely investigated prior to studying expression level change.
Conversely, normalizing by the abundance of an internal standard within a library (e.g.,
spike-ins) does not account for variation in the number of contributing cells across
treatments, and the inferred transcript abundance may be biased (Fomina-yadlin et al.,
2014). It is therefore critical to have information on cell density differences between
treatment groups when normalizing to an internal standard.
Following polyploidy, a balance of cell size and density effects, and changes in
allelic dosage could have profound effects on all facets of plant physiology. Across a
ploidal series in Atriplex confertifolia, cell density decreases with increasing ploidy
(Warner and Edwards, 1989). Despite harboring fewer cells per unit area, higher ploidal
levels of A. confertifolia are capable of higher photosynthesis per unit leaf area, due to
increased photosynthesis of tetraploid cells relative to diploid cells. Conversely, in
Medicago sativa, while tetraploid cells are also less densely aggregated than diploid
cells, photosynthesis per cell is increased in the tetraploid to yield diploid-like
photosynthetic output per unit leaf area (Warner and Edwards, 1993). In A. confertifolia
and M. sativa, cell size and density alone are not sufficient for predicting physiological
change following polyploidy. Instead, information on the interaction between cell density
50
and cell efficiency is needed to fully realize the physiological impact of polyploidy. In
light of these observations, studies of expression level change following polyploidy
should routinely investigate the interaction between gene expression across a unit of
biomass and per cell.
When discussing transcript abundance between samples of differing
transcriptome sizes and/or differing cell density, a clear nomenclature is critical. We
illustrate three separate ways of defining transcript abundance when both transcriptome
size and cell density vary between treatment groups (Figure 1). When RNAseq data
are normalized without an external standard (Figure 1A), differences in abundance
observed between two treatments reflect a change in transcript concentration. We
follow Coate and Doyle (2010), and refer to this type of comparison as ‘per
transcriptome’ changes.
Differences in expression level between treatments normalized to the abundance
of spike-ins reflect changes in transcript abundance relative to the abundance of spike-
ins. When spike-ins are added in equal amounts to samples derived from equivalent
volumes of tissue then changes observed following a spike-in-based normalization
indicate transcripts differ in abundance within a given volume of tissue (Figure 1B). We
refer to this comparison as ‘per biomass’. Finally, if the spike-in abundance is scaled by
a factor equal to cell density differences, expression level changes observed will reflect
changes in transcript abundance relative to the cell (Figure 1C). We will refer to this as
a ‘per cell’ comparison using a simplified terminology (Figure 1).
Tolmiea (Saxifragaceae) was chosen for investigation of the impact of the three
measures of assessment reviewed above because: 1) it is a clear diploid (T.
51
diplomenziesii) and autotetraploid (T. menziesii) system; 2) there is strong support for a
single origin of the autotetraploid (Soltis and Soltis, 1988; Soltis et al., 1989; Visger et
al., 2016). In addition, examination of expression level changes following autopolyploidy
is less problematic than similar investigations within allopolyploid systems for several
reasons. First, autopolyploidy results in a duplication of a single genome, rather than the
merger and duplication of two divergent genomes (as in allopolyploidy). Hence, a single
diploid-based mapping reference can be used for both the diploid and the
autotetraploid. Additionally, there are no homeologues to be concerned with in an
autopolyploid, which substantially reduces the potential for biased mismapping of
paralogous reads. A single origin of the polyploid is also an advantage for exploring
issues pertaining to transcriptional amplification and reduces analytical complexity. For
example, multiple, independent origins of a polyploid may have differing effects on
transcriptome size. Thus, an investigation accounting for transcriptome size biases
should start with a single origin autopolyploid system.
Here we leverage spike-in RNA standards and multiple normalization methods to
characterize for the first-time gene expression across three biological scales in a natural
system. Through this multi-scale comparison, we investigate how polyploidy has
influenced transcriptional change in gene pathway stoichiometry, cellular expression
levels, and transcript abundance across leaf organ tissue. Finally, we synthesize the
changes represented across all three scales and place our findings within the context of
ecophysiological data for Tolmiea.
Materials and Methods
Sampling
52
Plants were collected from three geographically separate natural populations of
both T. diplomenziesii and T. menziesii (Figure 2). Taking advantage of the ability of
Tolmiea to reproduce via plantlet formation, each individual collected in the field was
subsequently propagated in quadruplicate in a greenhouse at the University of Florida.
The resulting plantlets were then grown to maturity under standardized conditions within
a common garden greenhouse at the University of Florida. One of four replicate plants
from two T. menziesii populations died prior to sampling.
Each of the 22 mature plants was sampled for equivalent volumes of leaf tissue
using a 8 mm diameter cork bore. The tissue was then flash frozen in liquid N2 and
stored at -80C until RNA extraction. Total RNA was extracted from this tissue using the
CTAB and Trizol method of Jordon-Thaden et al. (2015; protocol number 2) with the
addition of 20% sarkosyl. DNA was removed using a Turbo DNA-free kit (Invitrogen).
Following the manufacturer's recommendations, the total RNA was spiked with 4 ul of
1:100 diluted ERCC RNA Spike-in mix (Ambion). RNAseq libraries were then built using
the TruSeq kit (Illumina); 100bp paired-end sequencing was performed using an
Illumina HiSeq at the Beijing Genomics Institute.
Quantifying cell density
To quantify per cell changes it was necessary to account for both transcriptome
size differences and cell density differences between diploid and autotetraploid Tolmiea.
A potential source of uncertainty is our estimation of cell density differences used to
normalize our reads on a relative per cell basis. To decrease the likelihood of
misrepresenting cell density differences, we characterized cell density using both a
DNA/RNA co-extraction and cell counting as described below.
53
Duplicate leaf punches from each sample that were included in the differential
expression analysis were used for co-extraction of DNA and RNA. We first followed the
Jordon-Thaden et al. (2015) method #2 with 20% sakrosyl. Following the CTAB
incubation the supernatant was split into two equal aliquots; for one aliquot we used the
Jordon-Thaden et al. (2015) method for RNA extraction, and for the other aliquot we
followed the Doyle and Doyle (1987)method for CTAB DNA extraction. DNA
concentrations were quantified with dsDNA broad-range chemicals using a Qubit (Life
Technologies). DNA concentration was placed into a 1C context by dividing by ploidal
level. The 1C DNA concentration was used to infer the relative difference in cell density
of the leaf tissue contributing to RNA extraction between T. menziesii and T.
diplomenziesii. To validate this approach, we also directly estimated cell density per unit
area. Leaf punches two cm in diameter collected from 10 diploids and 11 tetraploids
were digested in 500ul of 10% chromic acid until cells were fully dissociated (Brown and
Rickless, 1949; Ilut et al., 2012). Each individual was assayed twice, and the number of
cells in the suspension was counted twice for each assay using 10ul aliquots in a
hemocytometer. Statistical analyses of 1C DNA concentration and cell density were
performed using a linear mixed-effects model implemented in JMP (version 12; SAS
Institute, Cary, NC, USA) with individual as a random effect and ploidal level as a fixed
effect. All datasets were tested for normality using a goodness of fit test, and if
normality was rejected the data were log transformed.
Differential expression analysis
Raw reads were cleaned using CutAdapt (Martin, 2011) and Sickle (Joshi and
Fass, 2011). A Tolmiea reference transcriptome was generated from concatenated
54
reads taken from all samples (with the spike-in reads removed) and in silico read
normalization was employed using Trinity)(Grabherr et al., 2011). Extremely low
expressed isoforms were removed (< 1 transcript per million), and the remaining
transcriptome was annotated using the Trinotate pipeline
(http://trinotate.sourceforge.net/). Trimmed reads for each sample were mapped to a
concatenation of the Tolmiea transcriptome with isoforms clustered together (using the
Trinity ‘gene’ option) and the publically available ERCC spike-in reference using
Bowtie2 (Langmead and Salzberg, 2012), and read counts were extracted using
eXpress (Roberts, 2013).
Read count normalization and differential expression analyses were conducted
using Limma-Voom (Ritchie et al., 2015). A per transcriptome normalization was
implemented using the total library following the removal of spike-in count data to
compute normalization factors. A per biomass normalization used only spike-in count
data to compute normalization factors. A per cell normalization used the spike-in count
data following in silico adjustment of tetraploid spike-in abundance using the difference
of diploid vs tetraploid cell density (65% -- see quantifying cell size and density methods
and results). Following each normalization, using the plotMDS function, a
multidimensional scaling plot was generated from 500 loci exhibiting the highest
expression level variation. Transcriptome size change was approximated using the sum
of normalized read counts per cell, however Limma-Voom normalizations use log-
counts which are not application to straight summation – we used the DEseq package
(Anders and Huber, 2010) to compute per cell normalized counts for this purpose only.
Next, a differential expression analysis was run using the above described
55
normalization approaches, with loci identified as differentially expressed (DE) using a
0.05 p-value, 0.05 false discovery rate, and 1 log fold change (logFC) cutoff.
Differentially expressed loci were binned both broadly across the three normalization
methods, and more finely to characterize interplay between the three normalization
results using gplots (Warnes et al. 2009). Gene ontology (GO) terms were extracted
from the mapping reference’s Trinotate annotations. Each of the fine-scale bins of
DEGs were tested for functional enrichment using GOSeq (Young et al., 2010).
Results
Quantifying cell size and density
The mean diploid and tetraploid 1C DNA concentrations per leaf punch extraction
were 1.32591 +/- 0.09439 ug/ml and 0.76432 +/- 0.0619 ug/ml, respectively; these
differed significantly (p < 0.0001) (Figure 3A). The mean tetraploid 1C DNA per punch
was 57.6% of the diploid value. Following a tissue digestion, the estimated values of
mean number of cells per leaf punch in diploids and tetraploids were 412,146 +/- 38,002
and 268,958 +/- 20,522, respectively. These values also differed significantly (p =
0.0129). The tetraploids on average had 65.3% as many cells per the same area (as
determined by a leaf punch) as found in the diploids (Figure 3B).
Our two methods for charactering cell density differences both revealed similar
reductions in tetraploid cell density; tetraploids possessed ~58% and 65% of the diploid
density, based on the DNA/RNA co-extraction and cell-count results respectively. It is
reassuring that the DNA/RNA co-extraction and cell counting methods of estimating cell
density yielded similar results. The cell count-based estimates revealed a slightly
smaller difference than the extraction-based estimate (~65% vs. ~58%). One
explanation for this slight difference is that there may be ploidy specific differences in
56
nucleotide extraction efficiency. We elected to normalize our per cell analysis by
applying a 0.65 factor to the per cell normalization factor of the tetraploid samples
reflecting the more conservative estimate of cell density differences—however, both
estimates of cell density reveal the same the major conclusions of this study.
Differential expression analysis
We obtained an average of 25 million reads per sample after removing low-
quality reads. The Tolmiea reference transcriptome assembly resulted in 58,046
isoforms binned within 28,467 clusters (henceforth genes) with an N50 of 1,821bp. After
read mapping, 26,816 genes had at least 5 non-zero counts and were used for
downstream differential expression analyses. A total of 15,205 genes was annotated
according to gene ontology using Trinotate.
A multidimensional scaling plot of the 500 loci with the highest expression level
variation revealed that all three normalization methods (per transcriptome, per cell, and
per biomass) performed well at clustering members from the same population with one
another (Figure 4A-C). It is also noteworthy that across the 500 most variable loci, the
diploid samples cluster by population, while the tetraploids show little population
differentiation. After summing the read counts normalized per cell for each sample, we
found that the mean tetraploid transcription per cell (henceforth transcriptome size) was
2.1 times higher than the diploid mean (Figure 5). Additionally, the total transcriptome
size per cell was highly variable, more so in the tetraploids compared the diploids
(20,815,579 and 11,799,791 normalized counts respectively). Across the three
normalization methods, the differential expression analysis found the tetraploid relative
to the diploid had 1,559 up- and 1,071 down-regulated genes per transcriptome, 1,440
57
up- and 1,550 down-regulated genes per biomass, and 3,005 up- and 751 down-
regulated genes per cell (Figure 4D-F). Across the three different normalization
methods, we found 4,555 unique loci were DE under one or more methods (Figure 6).
Finer binning of the interactions between normalization methods revealed the majority
of DEGs to be either up-regulated in the tetraploid across all normalizations (1,392 –
Figure 7A) or only up-regulated per cell (1,398 Figure 7D).
Discussion
Gene expression changes following autopolyploidy
This is the first study to leverage synthetic RNA standards to characterize
expression level change per transcriptome, per biomass, and on a relative per cell basis
in a cross ploidy comparision. Through the use of a novel three normalization approach
and characterization of gene expression level change between diploid and
autotetraploid Tolmiea, we found 4,555 out of 26,816 loci were DE between ploidal
levels (~17% of the transcriptome) and found four notable trends. First, the per
transcriptome normalization, the normalization method researchers typically use,
captured the fewest DEGs and failed to detect any DEGs not found by the other two
methods. Second, most differential expression occurs on a per cell basis, and there is a
clear unbalanced distribution of up- vs down-regulation in the autotetraploid relative to
the diploid, with 3,005 up- vs. 751 down-regulated DEGs per cell. Third, in the
tetraploid, many transcripts that were up-regulated per cell appear to compensate for a
decreased cell density, resulting in a conservation of expression level per biomass
relative to the diploid (see Figure 8). Loci exhibiting conservation per biomass were
significantly enriched for functions related to photosynthesis and the chloroplast. Fourth,
we saw transcriptome size varied substantially across our dataset, and found a
58
significant increase in the inferred transcriptome size of the tetraploid relative to the
diploid (Figure 5). Below we discuss each of these four trends in greater detail.
Autopolyploids have rarely been compared to their diploid progenitors with
respect to expression level divergence (e.g., Stupar et al., 2007; Del Pozo and Ramirez-
Parra, 2014; Zhang et al., 2014). These few previous diploid-autopolyploid comparisons
found that autopolyploids tend to deviate from diploid-like gene expression levels across
1-10% of the transcriptome (~6% in Paulownia fortunei – Zhang et al., 2014; ~10% in
Solanum phureja – Stupar et al., 2007; ~1-4% in Arabidopsis – Del Pozo and Ramirez-
Parra, 2014). However, none of these comparisons was normalized using spike-in
standards; instead they used what is referred to here as a per transcriptome
comparison. In fact, spike-ins have rarely been used in any evolutionary comparisons,
and have primarily been adopted for use in studies of model organisms (e.g., Fomina-
yadlin et al., 2014). Unlike the spike-in derived per biomass and per cell normalized
transcript counts, the results of per transcriptome normalizations reflect concentration
changes and are not a proxy for absolute expression. Therefore, the results of previous
autopolyploid expression studies must be interpreted as changes in transcript
concentration rather than absolute abundance.
Considering only the results of our per transcriptome normalization,
approximately 9% of the Tolmiea transcriptome was differentially expressed on a
concentration-basis. Our discovery that 9% of the Tolmiea transcriptome differs in
concentration between the diploid and autotetraploid species is in line with results for
other diploid--autopolyploid pairs mentioned above. This finding suggests that in
general, only a small fraction of the transcriptome, less than 10%, responds to
59
autopolyploidization through novel alterations to transcript concentration. Despite
changes in transcript abundance per transcriptome representing less than 10% of all
loci, concentration-based changes could have important consequences regarding the
stoichiometry of gene expression pathways. Unfortunately, although Tolmiea is a good
evolutionary model it is not a genetic model; in a non-model system such as Tolmiea,
the ability to investigate specific pathways and make inferences regarding physiological
impact is severely hampered. However, it would be important for future studies to test
whether members of a given pathway respond similarly with respect to the maintenance
of transcript abundances relative to the transcriptome, cell, or biomass.
It is also notable that although ~9% of the loci examined in Tolmiea were
differentially expressed per transcriptome, none of these DEGs were uniquely
recovered only from the per transcriptome analysis. The majority of per transcriptome
DEGs (2,106 of the 2,630) were differentially expressed at a high enough magnitude to
be detected by all three normalization methods. These results suggest that the loci
typically identified as DE in previous studies of polyploid gene expression represent only
changes in expression level extreme enough to be detected through a significantly
altered concentration. Therefore, examination of more subtle expression level change
requires the use of normalization approaches that allow for quantitative comparisons of
transcript abundance.
When we compared expression level per cell, we found that between diploid and
tetraploid Tolmiea ~14% (3,756 loci) of the transcriptome was maintained in different
abundances per cell. Importantly, the direction of expression level change in the T.
menziesii relative to the diploid was extremely unbalanced. Nearly all per cell DEGs
60
were up-regulated in T. menziesii (3,005 loci), with only 751 down-regulated. In
addition, 1,398 of the 3,005 per cell DEGs up-regulated in the tetraploid were unique to
the per cell normalization, and not recovered under either per transcriptome or per
biomass normalizations (Figure 7D). Taken together, it appears that most differential
expression in Tolmiea represents a pattern consistent with increased gene expression
level in the tetraploid correlating with an increase in ploidal level. This result would not
have been detected under a typical library size normalization method, as the expression
of these loci are not significantly altered in concentration relative to the whole.
The over-abundance of up-regulation per cell in the tetraploid may be serving as
a cell density compensation mechanism. In other words, there is tendency in T.
menziesii for a conservation of gene expression level per biomass through novelty at
the cellular level (henceforth per biomass conservation)(Figure 8). Approximately 1,398
loci, or 5.2% of all loci, in T. menziesii exhibit per biomass conservation (Figure 7D).
This buffering effect is achieved by what appears to be precise per cell up-regulation in
T. menziesii mirroring the cell density decrease relative to T. diplomenziesii. Fifteen
functional categories were significantly over represented among the loci exhibiting per
biomass conservation. Of these, seven were related to either the chloroplast or
photosynthesis. Whether there is selective pressure to conserve expression per
biomass is unclear, but alteration of photosynthesis either per cell or per biomass
appears to be a reoccurring theme across diploid/polyploid comparisons (e.g., Warner
and Edwards, 1993; Vyas et al., 2007; Coate et al., 2013). For example, Warner and
Edwards (1993) revealed photosynthetic conservation per biomass in M. sativa, but an
overall increase in photosynthesis per biomass following polyploidy in A. confertifolia. A
61
clear trend of the effects of polyploidization on photosynthesis per biomass has yet to
emerge, and like many aspects of polyploidy, it may be lineage specific and/or require
results from additional study systems (Soltis et al., 2016).
Diploid and tetraploid Tolmiea occur under similar light regimes in nature (Visger
et al., 2016), and previously collected physiological data revealed no significant
difference in photosynthetic rate per leaf area (Visger et al., 2016)(Figure 9). In Tolmiea,
conservation of expression level per biomass may be a mechanism for the maintenance
of optimal photosynthesis per biomass, facilitating the ecological conservation of light
preference in Tolmiea. To determine if photosynthesis-related functional enrichment of
conservation per biomass is indeed a key underlying molecular mechanism for buffering
cell density as it pertains to photosynthesis, additional autopolyploid systems should be
similarly studied. Revisiting the work of Warner and Edwards (1993) using our spike-in
standard-based gene expression approach, should also show a similar conservation of
gene expression per biomass in polyploid M. sativa. Conversely, in A. confertifolia
where polyploidy increases photosynthesis per leaf area, we might expect
photosynthesis-related gene expression per cell to be increased by a factor greater than
the diploid/polyploid cell density difference (Warner and Edwards 1993).
An initial motivation for utilizing a spike-in approach to read count normalization
was to tease apart library size variation from transcriptome size differences between
diploid and tetraploid Tolmiea. We found that when normalizing read counts per cell, the
mean transcriptome size of the tetraploid is over twice that of the diploid (Figure 5).
This difference in transcriptome size should be qualified, as the variation within and
between populations is quite large, though the mean transcriptome size still significantly
62
differed with ploidal level. Notably, we also observed that the variability of transcriptome
size was greater in the tetraploids. The diploid populations all exhibited a similar degree
of variation in transcriptome size, while tetraploid populations represented both the least
and greatest population level plasticity in transcriptome size (Figure 5). Excluding all
other results presented in this study, the variability of transcriptome size alone should
be motivation enough for researchers of polyploidy to adopt a spike-in based approach.
The benefits and caveats of spike-in RNA standards in biology
This study is not the first to apply synthetic RNA spike-in standards in dealing
with a transcriptome-wide effect (e.g., see Lovén et al., 2012; Fomina-yadlin et al.,
2014). However, spike-ins have rarely been used to address questions of evolution in
natural populations. This is the first study to: 1) leverage spike-in standards in a cross
ploidy comparison, and 2) quantify expression level on three different, biologically
relevant scales. By using multiple read count normalizations, with and without spikes-in
standards, we investigated the interaction of expression level per cell and per biomass
between diploid and autotetraploid Tolmiea. Had this study been performed in lieu of
spike-in standards, the results would have been limited to those of the per transcriptome
normalization.
Spike-in normalization is most valuable when evaluating gene expression level
changes between two groups that have differing cell density and/or transcriptome size.
By normalizing RNAseq count data by the abundance of spike-in standards, sequencing
depth and transcriptome size are effectively disentangled. Methods employed by
previous comparative studies of diploid/polyploid pairs were limited to quantifying
transcriptional changes on a concentration-basis only. Concentration-based
63
comparisons, while useful for inferring alterations of pathway stoichiometry, are
effectively blind to large proportions of the transcriptome exhibiting an additive
expression level response to polyploidy. That is, if the expression of many genes is
increased in a single direction and is commensurate with the increase in ploidal level,
then the impact on any single gene’s concentration will be minimal. An additional
advantage of employing a spike-in normalization is that transcript abundance can be
independently quantified relative to biomass and relative to the cell. Spike-in reads can
also be removed for some downstream analyses, allowing for a typical library-size
normalization so that concentration-based changes may be characterized as well.
The three normalization approaches presented here are all individually
informative, and the decision to include any or all of them should be guided by the
research question. For example, if the research question revolves around the bulk
production of a compound, evaluating changes in expression level per biomass may be
the best approach. Research questions focusing on complex gene pathways may be
better served by an analysis of expression level per cell. Information on potential
changes in expression level stoichiometry can be gained using traditional comparisons
per transcriptome. Additionally, as demonstrated by our study of Tolmiea, the
interaction among multiple normalization approaches can be equally, or more,
informative as any single approach.
While the use of non-concentration-based normalizations can enable researchers
to address new questions, there is an important caveat that could lead to potential
biases or increased uncertainty. A primary concern is that differences in RNA extraction
efficiency between treatment groups are difficult to tease apart from variation in total
64
transcriptome size. In much the same way that variation in transcriptome size
influences estimates of expression level, differences in extraction efficiency could bias
expression level calculations for per-cell and per-biomass analyses. For example, if
RNA extraction were half as efficient in one treatment group versus another, then
differential expression analyses per cell would only consider half of the actual transcript
abundance of one group relative to the other. Future approaches should consider
partially accounting for this issue through the addition of a second unique set of spike-
ins prior to RNA extraction. Comparing the ratio of pre- versus post-extraction spike-in
abundance should highlight changes in extraction efficiency. However, even the use of
a second spike-in set will not account for different extraction efficiencies if those
differences arise from variation in cell lysis.
In summary, this study has demonstrated that the use of synthetic RNA spike-in
standards can be used to explore previously uninvestigated aspects of gene expression
level divergence in a comparison of a diploid and its autotetraploid derivative. To our
knowledge, this multiple normalization approach has recovered the largest fraction of a
transcriptome as DE in a diploid/autopolyploid species pair ever reported (~17% in
Tolmiea vs. up to ~10% in several other plant systems; Stupar et al., 2007; Del Pozo
and Ramirez-Parra, 2014; Zhang et al., 2014). Further, the methodology employed
allowed for a fine scale examination of how gene expression level divergence interacts
with cell density, revealing a mosaic of change in transcript concentration, abundance
per cell, and abundance across tissue.
While we compared a diploid-autopolyploid, transcriptome size variation is rarely
investigated and could be wide spread in biological systems at diverse scales. To date,
65
nearly all global gene expression studies have used normalization methods that
implicitly assume transcriptome size is invariable, yet this assumption is not empirically
supported. Examples of studies where transcriptome size variation might be likely
include (but is by no means limited to) comparisons between related species, different
developmental stages, and across stress treatments. Yet even in these cases the
potential for transcriptome size variation remains ignored and uninvestigated. If
transcriptome size is in fact invariable between two experimental treatment groups, than
following our proposed methodology, the results of the per-transcriptome and per-cell
comparisons should be identical. It is of critical importance that researchers making
comparisons using RNAseq data, particularly in the broad suite of examples noted
above, avoid making the assumption that transcriptome size is invariable and instead
employ a multiple normalization approaches, as we do here.
66
Figure 3-1. A simplified example of how spike-in standards can be used during read normalization to enable comparisons of expression level at different biological scales between a hypothetical diploid-polyploid pair with differing cell density. The large circles represent a unit of biomass and contain a number of cells (green squares). Beneath each circle is a depiction of how the read normalizations are calculated. Using a per transcriptome normalized analysis, the ratio of target transcripts to the total transcriptome is compared. While per biomass normalization uses the ratio of the transcript of interest to the spike-in transcripts. The per cell normalization also uses the ratio of the transcript of interest to spike-in transcripts, but scales the spike-in transcript abundance by cell density, represented here by multiplying the spike-in abundance by the number of contributing cells. Whether the transcript of interest would be found as not differentially expressed or higher/lower expressed in the polyploid under each normalization is indicted using ‘=’, ‘<’, or ‘>’ respectively.
Non-target transcript
Transcript of interest
=Per-transcriptome normalization:DE analysis tests whether the transcript of interest ismaintained in a different abundence relative to the totaltranscriptome.
"diploid" "polyploid"
<per
per
Per-biomass normalization:DE analysis tests whether the transcript of interest ismaintained at different levels within a given biomass
<
per
÷ ÷Per-cell normalization:DE analysis tests whether a given transcript ismaintained at different levels within a cell
Spike-in standard
A
B
C
per
per
per
67
Figure 3-2. Generalized distributions of Tolmiea menziesii and Tolmiea diplomenziesii. The population sources for plants used in this study are represented as red triangles (T. diplomenziesii) and blue squares (T. menziesii).
68
Figure 3-3. Results of ploidy variation in leaf cell density using, A) 1C DNA
concentration following a DNA/RNA co-extraction, and B) cell counts per 2cm diameter leaf punch.
T. diplomenziesii T. menziesii T. diplomenziesii T. menziesii
0.6
0.8
1.0
1.2
1.4
1.6
1C
DN
Au
g/m
l
Estim
ate
dcells
pe
r2-c
mdia
mete
rle
afp
un
ch
2e
+0
54e
+05
6e
+0
58
e+
05A B
69
Figure 3-4. Results from multiple differential expression analysis. Multi-dimensional scaling (MDS) plots A-C cluster individual based on the 500 most variable loci, with color indicating ploidal level and shape reflecting population of origin. MA plots D-F show every locus in the Tolmiea transcriptome (represented as dots), with log fold expression level change in the polyploid relative to the diploid on the y-axis and average expression level on the x-axis-- red indicates statistical significance.
-4 -2 0 2
-3-2
-10
12
3
Leading logFC dim 1
Average Log Expression Average Log Expression Average Log Expression
Lea
din
glo
gF
Cd
im2
Log
Fo
ldC
han
ge
Per transcriptome Per biomass Per cell
-4 -2 0 2
-3-2
-10
12
3Leading logFC dim 1
-4 -2 0 2
-3-2
-10
12
3
Leading logFC dim 1
A B C
70
Figure 3-5. Sum of read counts normalized per cell, and clustered by population of
origin. Diploid and tetraploid mean significantly differed (p < 0.008).
Population0
1
2
3
4
5
6
7
8S
um
ofn
orm
aliz
ed
co
un
ts1e7 Total reads normalized per Cell
Tolmiea diplomenziesii
Tolmiea menziesii
71
Figure 3-6. Venn diagram contrasting the three normalization methods. Numbers within
the different sections indicate loci that were identified as being differentially expressed between Tolmiea menziesii and T. diplomenziesii.
Per Cell
1,398
Per Biomass
442
167
85
357
2,106
Per Transcriptome
0
n = 4,555
Differentially expressed genes by normalization method
72
Figure 3-7. Loci binned by their DE categorization across the three normalization approaches. The number of loci belonging to each bin and the results of GO enrichment analyses are reported below the corresponding bin. Bins containing no loci are not shown.
PerTr
ansc
ripto
me
PerCel
l
PerBiom
ass
Up-regulated in 4x
Not DE
Down-regulated in 4x
Per
Tran
scrip
tom
e
Per
Cel
l
Per
Biom
ass
Number/Proportion of loci:Signific
a
ntly enriched GO categories
Number/Proportion of loci:Signific
a
ntly enriched GO categories1,392/5.19%: None
714/2.66%: None 37/0.138%: None 442/1.65%: None
167/0.623%: None 48/0.179%: None
A B C D
E F G HUp-regulated in 4x
Not DE
Down-regulated in 4x
1,398/5.21%: Photosystem I, Photosynthesis,light harvesting, Chlorophyll binding, Protein-chromophore linkage, Photosystem II, Auxinpolar transport, Protein xylosyltransferaseactivity, Chloroplast thylakoid membrane,Anthocyanin accumulation in tissues, Auxin-activated signaling pathway, Lateral rootformation, Positive gravitropism, Auxin influxtransmembrane transporter, Chondroitinsulfate biosynthetic, Chloroplast thylakoid
357/1.33%: Drug transmembrane transportactivity, Hydrogen ion transmembranetransport, Proanthocyanidin biosyntheticprocess, Solute: proton antiporter activity,Maintenance of seed dormancy
73
Figure 3-8. A simplified example of two observed interactions between expression level and cell density. Conservation of gene expression per biomass occurs when expression level per cell in samples with lower cell density is up-regulated enough to yield equivalent levels of transcript per unit biomass.
per10x
per
per10x
per
Conservation of gene expression level per biomass(per biomass conservation)
"diploid" "polyploid"TranscriptCellLeaf punch
74
Figure 3-9. Adapted from data collected from Visger et al. 2016. Tolmiea diplomenziesii and T. menziesii did not significantly differ in photosynthetic output under common garden conditions in the greenhouses of University of Florida.
Tolmiea diplomenziesii Tolmiea menziesii
24
68
10
Photosynthetic rate
µm
olC
O2
m-2
s-1
75
CHAPTER 4 DIFFERENTIAL DROUGHT RESPONSE AND TRANSCRIPTOME SIZE PLASTICITY
BETWEEN DIPLOID AND AUTOPOLYPLOID TOLMIEA
Introduction
Water availability is one of the most critical abiotic factors in determining habitat
suitability for terrestrial green plants, as water is essential for a variety of important
physiological functions (e.g., Sack and Holbrook, 2006). Drought conditions can
severely impact non-arid-adapted plants, with effects including, but not limited to,
inducement of stomatal closure (Cornic, 2000; Tombesi et al., 2015), impairment of
photosynthesis (Flexas and Medrano, 2002), accumulation of harmful reactive oxygen
species (ROS) (Farooq et al., 2009; Jubany-Marí et al., 2010), and ultimately death. In
addition to the importance of water availability for determining habitat range and
suitability for naturally occuring plant populations, the need for sufficient water for crops
can lead to trade-offs between economic and environmental impacts (Pimentel et al.,
2004). Within the next 30-90 years, climate change is expected to drastically alter the
frequency and degree of drought conditions on a global scale (Dai, 2012). Therefore,
understanding how different groups of plants respond to decreased water availability
could help predict future impacts on patterns of biodiversity response to climate change,
as well as mitigate some of the environmental costs associated with agriculture.
Whole-genome duplication (WGD; polyploidy) involves the addition of one or
more complete sets of chromosomes to an organism’s normal genetic composition.
Some major lineages appear to be more tolerant of WGD than others, and have
evolutionary histories in which polyploidy has played a major evolutionary role. These
lineages include vertebrates (e.g., Braasch and Postlethwait, 2012; Cañestro, 2012),
fungi (Hudson and Conant, 2012), ciliates (Aury et al., 2006), and, most notably,
76
flowering plants, which seemingly thrive following WGD, as evidenced by the numerous
polyploidization events that have been identified throughout the angiosperm tree of life
(e.g., Cui et al., 2006; Soltis et al., 2009; Van de Peer et al., 2009; Jiao et al., 2011,
2012; Van de Peer, 2011; Amborella Genome Project, 2014).
The physiological effects of polyploidy can be dramatic, but the specific impacts
are often varied and unpredictable (see Soltis et al., 2014, 2016). In some cases,
polyploidy yields immediate changes in physiology; for example, some polyploids exhibit
a divergent response to changing water availability (e.g., Senock et al., 1991; Hao et al.,
2013). While far from universal, increased stomatal guard cell length is often considered
a diagnostic trait of polyploids (Masterson, 1994; Hodgson et al., 2010). From a
physiological perspective, larger stomatal components can alter water-use efficiency as
a result of either a larger opening or an alteration of the boundary layer dynamics; this
increase in size can be buffered, however, by an accompanying decrease in stomatal
density (Mishra, 1997; Beaulieu et al., 2008).
In addition to stomatal guard cells, other cells, including in the mesophyll
(Sugiyama, 2005), can also be affected following WGD. Vessel architecture can also be
altered by polyploidy and this would in turn influence the incidence of vessel embolisms,
leaf water potential, and other hydraulic characters (Sack and Frole, 2006). However,
few studies have investigated the underlying transcriptional effect of polyploidy and how
it influences drought tolerance (e.g., Del Pozo and Ramirez-Parra, 2014).
WGD spans a continuum from within species (autopolyploidy) to between
species (allopolyploidy). Despite allopolyploidy and autopolyploidy occurring with near
parity in Angiosperms (Barker et al., 2016), nearly all of the recent genetic and genomic
77
insights have been obtained from allopolyploid systems—as a result, we have learned a
great deal about allopolyploidy, but we still know very little about the genomic
consequences and evolutionary impact of autopolyploidy (e.g., Soltis et al., 2007, 2010;
Doyle et al., 2008; Parisod et al., 2010). While allopolyploidy is without doubt the more
heavily studied form of polyploidy, it is exceedingly difficult to tease apart the effects of
polyploidy per se from the effects of hybridization, and very few studies have done this
successfully (e.g., Chelaifa et al., 2010). Autopolyploidy results in additional alleles for
every gene within the genome and is characterized by random pairing of homologous
chromosomes, typically forming multivalent arrangements at meiosis (reviewed in Tate
et al., 2005). Random chromosome pairing leads to polysomic inheritance (rather than
the disomic inheritance typical of diploids and allopolyploids), which not only represents
a method to distinguish between allopolyploids and autopolyploids but also has major
evolutionary implications.
Allopolyploids may ultimately undergo fractionation, diploidization, and loss of
extra gene copies. However, assuming polysomic inheritance remains in place, an
autotetraploid will retain four alleles per locus through time and not experience
fractionation – in other words, autopolyploidy should produce longer-lasting dosage
effects than allopolyploidy. Biochemical pathways can respond to an increase in allelic
dosage in several ways: dosage compensation (total expression level similar to that of
the diploid parent), strict additivity (double the diploid expression level), total expression
between compensated and additive extremes, an inverse relationship with the
autotetraploid displaying lower expression, or an expression level intermediate to these
extremes. For example, gene expression across 13 loci in synthetic autotetraploids of
78
maize ranged from 0.5 to 1.5 times those of diploid levels (Yao et al., 2011). In contrast,
three regulatory cell cycle genes in synthetic autotetraploid Arabidopsis thaliana all
displayed a strict doubling of expression relative to the diploid progenitor, suggesting a
link between gene function and dosage regulation (Li et al., 2012). Restoration to
diploid-like expression levels has often been observed in the few studies conducted on
synthetic autotetraploids (e.g., Zea mays; Guo et al., 1996), whereas a change in
expression level, as with these cell cycle genes, seems to vary across systems from
~10% of investigated loci in autopolyploid Solanum tuberosum (Stupar et al., 2007) to
~75% in allopolyploid Zea mays (Riddle et al., 2010), though expression changes
appear to rarely exceed a 2-fold difference (Birchler et al., 2003). The resulting
alterations in levels of gene product could have profound impacts on polyploid
physiology and ecology.
Immediately following WGD, allele copy numbers should scale linearly (e.g., 2-
fold nuclear material), while increases in cell volume following WGD do not (e.g., 1.5-
fold cell volume increase); an altered ratio of nuclear material to cell volume could
influence intracellular functions under concentration-dependent control (Levin, 1983;
Storchová et al., 2006). A combination of cell size effects and changes in gene dosage
following polyploidy may have serious implications for stress response. For example,
mRNA flow from the nucleus to the cytosol could be influenced by an alteration of
nuclear pore density (see Levin 1983). The implications of stress response differences
across ploidal levels are three-fold, with changes potentially occurring per
transcriptome, per cell, and per unit biomass (see Chapter 3). Each different scale at
which gene expression level might diverge could have impacts on drought response
79
physiology. The concentration-based changes uncovered through a per-transcriptome
comparison can alter the stoichiometry of gene pathways. Per-cell and per-biomass
comparisons reflect changes in the abundance of transcripts, which could influence
intracellular and intercellular stress response signaling.
The angiosperm genus Tolmiea (Saxifragaceae), comprising only two species, a
diploid (T. diplomenziesii) and its autotetraploid derivative (T. menziesii), has long been
recognized as one of the clearest examples of autopolyploidy in nature (Soltis and
Soltis, 1988). Furthermore, the autotetraploid appears to have originated only a single
time (Soltis et al., 1989). Recently, Tolmiea has been treated as an emerging
evolutionary model for the study of autopolyploidy in natural systems (Visger et al.,
2016; Visger et al., submitted). Differences in water-use efficiency and water availability
have been implicated as key components in the niche divergence exhibited by T.
menziesii and T. diplomenziesii (Visger et al., 2016). Transcriptomic investigations
demonstrated that T. menziesii transcribes over 20% of its genes differentially relative to
T. diplomenzii (Visger et al., submitted). However, previous transcriptomic work on
Tolmiea was a general exploration of gene expression level response to polyploidy and
did not incorporate a physiological stress component. These prior ecological and
physiological investigations, combined with the clear autopolyploid natur, and single
origin of autotetraploid in Tolmiea make it an ideal natural system in which to study the
impact of WGD on the molecular response to drought stress.
Here we investigate the transcriptomic basis of the ecophysiolgical divergence in
drought response between diploid and polyploid Tolmiea. Using polyethylene glycol
(PEG)-treated hydroponic cultures, we subjected T. diplomenziesii and T. menziesii to
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negative osmotic potential, inducing extreme drought stress. We then compared gene
expression over time in response to our treatment and determine the gene functions
most likely to contribute to the physiological differences between T. diplomenziesii and
T. menziesii. Using recently developed methods, we accounted for variation in
transcriptome size and cell size/density, enabling our comparisons of gene expression
to take place in the context of change per cell, per biomass, and per transcriptome (see
Coate and Doyle, 2010, 2015; Lovén et al., 2012; Visger et al., submitted).
Materials and Methods
An assembly-free Tolmiea reference transcriptome was generated using single-
molecule transcript sequencing via PacBio (Pacific Bioscience, Menlo Park, CA,
USA)(Isoform sequencing; IsoSeq). Total RNA was extracted from a diploid individual
using the CTAB and Trizol method detailed below (Jordon-Thaden et al., 2015), and
DNA was removed using a Turbo DNA-free kit (Invitrogen, Carlsbad, CA, USA). Three
size selections were performed using SageELF on the total RNA, yielding bins of
transcripts ~0.8-1 kb, ~1-2 kb, and ~2-5 kb in length. Each size fraction was sequenced
using 2 SMRT cells on a PacBio instrument at the University of Florida Interdisciplinary
Center for Biotechnology (UF ICBR). Full-length transcript sequences were obtained
using the ToFu pipeline described by Gordon et al. (2015). Briefly, the classify function
was implemented through SMRT Analysis software (Pacific BioScience), which uses
information on adapter location in raw reads to generate circular consensus sequences
(CCS). Only CCS representing full-length transcripts were retained, using the presence
of the 5’ primer and a polyA tail followed by the 3’ primer as the criteria for determining
full-length. Next, full-length CCS derived from the same isoform were clustered and
error-corrected using ICE and Quiver, also implemented through the SMRT Analysis
81
software (Pacific BioScience). The resulting set of sequences was annotated with
Trinotate (http://trinotate.sourceforge.net/) and used downstream as the reference
transcriptome.
Tolmiea can reproduce vegetatively via plantlets. Four diploid and four tetraploid
plantlets were cultivated hydroponically for four months on Leca clay pellets in a 50%
Hoagland’s no. 2 basal salt solution (Sigma-Aldrich, St. Louis, MO, USA). To reduce the
likelihood of local adaptation confounding polyploidy-based inferences, but at a potential
cost of detecting small changes in expression level, both the diploid and tetraploid
plantlets were derived from two distinct well-separated populations each. Following the
methods of Liu et al. (2015), a drought response was induced in the plantlets through
the addition of PEG 6000 to the hydroponic solution (20% g/mL – resulting in
approximately -0.6 Mpa water stress). Leaf samples were collected from all eight plants
prior to the addition PEG 6000 (Day 0), and 24 hours (Day 1) and 48 hours (Day 2) after
PEG 6000 was added. Leaf tissue from each plant was sampled with an 8-mm-
diameter cork borer. The sample was flash frozen in liquid N2 and stored at -80 C prior
to RNA extraction. The CTAB and Trizol method of Jordon-Thaden et al. (2015; protocol
number 2) with the addition of 20% sarkosyl was used to extract total RNA, and a Turbo
DNA-free kit (Invitrogen) was used to remove DNA. The total RNA was spiked with 4 ul
of 1:100 diluted ERCC RNA Spike-in mix (Ambion), following the manufacturer's
recommendations. RNAseq libraries were constructed with dual-index barcodes by
RAPiD Genomics (Gainesville, FL, USA), and the libraries were pooled and sequenced
across four runs of 1x75-bp Illumina NextSeq at the UF ICBR.
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CutAdapt (Martin, 2011) and Sickle (Joshi and Fass, 2011) were used to clean
raw reads, and the cleaned reads were mapped to both the Tolmiea reference
transcriptome and the publically available ERCC spike-in reference using Salmon (Patro
et al., 2015). Read count normalization followed the three-normalization approach
proposed by Visger et al. (submitted), which was implemented using Limma-Voom
(Ritchie et al., 2015). In short, ERCC spike-in read abundances were leveraged to
normalize transcript counts per transcriptome, per biomass, and per cell (see Visger et
al., submitted). A multidimensional scaling (MDS) plot was generated from the 500 loci
exhibiting the most variation across the dataset using the plotMDS function in Limma-
Voom (Ritchie et al., 2015). Differential expression analyses of gene expression
response through time were conducted on each of the three normalized count datasets
using maSigPro (Conesa et al., 2006) using a general linear model with a negative
binomial family and a theta parameter of 10. Only loci with a minimum of at least 5
counts in at least 3 samples were analyzed. Genes exhibiting significantly different
responses between ploidal levels across the 3-day drought treatment were identified
(using a p < 0.05 and r2 > 0.6 cutoff) and subsequently grouped by response similarity
into six clusters using hierarchical clustering. The results of each differential expression
dataset, as well as each individual cluster of DEGs, were tested for functional
enrichment based on Gene Ontology (GO) assignments using GOseq (Young et al.,
2010).
We used ecological modeling to assess the impact of global climate change on
the future range of T. menziesii and T. diplomenziesii. Using MaxEnt (ver. 3.3.3k;
Phillips et al., 2004, 2006), we generated ecological niche models for T. diplomenziesii
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and T. menziesii using occurrence data and bioclimatic layers from Visger et al. (2016).
The ecological models were projected onto the predicted climate of the Pacific
Northwest under the CCSM4 rcp85 model of climate change for the year 2070.
Results
IsoSeq of the three size factors, 0.8-1 kb, 1-2 kb, and 2-5 kb, yielded 112,679,
83,615, and 26,467 full-length non-chimeric reads, respectively. The full-length reads
were clustered into 37,236 unique isoforms, with an N50 of 1,793. In total, 23,312
isoforms were successfully annotated for GO using Trinotate
(http://trinotate.sourceforge.net/).
The diploid and autotetraploid Tolmiea plantlets appeared to grow equally well in
the hydroponic solution. Neither species grew more quickly nor resulted in noticeably
larger plants than the other. After 48 hours of 20% PEG 6000 treatment, all diploid and
tetraploid plants were visibly wilted, indicating the treatment induced drought stress.
Following RNA extraction, library prep, and low-quality read filtering, RNA-seq
yielded an average of 44.9 (ranging from 24.1 to 102.0) million reads per sample.
However, the tetraploid individual 25b2 sample taken 24 hours post-drought failed to
sequence properly and was removed from subsequent analyses. On average, 88.3%
and 89.9% of reads per diploid and tetraploid sample, respectively, were successfully
mapped to the reference transcriptome. After removing transcripts without a minimum of
five counts in three individuals, we retained 25,037, 25,967, and 27,035 in our per-
transcriptome, per-biomass, and per-cell comparisons, respectively. The total
transcriptome size was calculated as the sum of transcriptome normalized per cell
(Figure 1 - plotted using the Python package SciPy - Oliphant, 2007). In total, the three-
normalization approach uncovered 2,495 transcripts, approximately 9.3% of the
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transcriptome, that responded differently to drought over time in diploid vs. tetraploid
Tolmiea. Consistent with the expectations of a single-origin autopolyploid, the MDS plot
(Figure 4-2) shows that tetraploid gene expression does not appear to have a
population effect, while the diploids were grouped by population membership. The per-
transcriptome comparison found that the concentration of 568 transcripts showed a
significantly differential drought response (DDR) over time in the diploid relative to the
tetraploid. These 568 per-transcriptome DDR transcripts were clustered into 6
response profiles (Figure 4-3). Between diploid and tetraploid Tolmiea, the per-biomass
comparison found that 1092 transcripts varied in their abundance per unit biomass in
response to drought over time. The 1092 per-biomass DDR transcripts were clustered
into 6 response profiles (Figure 4-4). Using the per-cell comparison, we found that 2170
transcripts displayed a differential change in transcript abundance per cell over time in
the diploid vs. tetraploid. The 2170 per-cell DDR transcripts were grouped into 5
response profiles due to 2 of the original 6 profiles (cluster 4 and cluster 5) exhibiting a
highly similar profile (Figure 4-5). After comparing the overlap between the three
normalization methods (Figure 4-6), we found 122, 148, and 1191 DDR transcripts
unique to the per-transcriptome, per-biomass, and per-cell comparisons, respectively.
We recovered 301 DDR transcripts across all comparisons, 588 were identified per
biomass and per cell, 55 per biomass and per transcriptome, and 90 per transcriptome
and per cell.
GO enrichment analyses revealed that certain response profiles across the three
normalization datasets were more likely than expected by chance to contain transcripts
associated with specific functions (see Figures 4-3, 4-4, 4-5). After 24 hours of drought
85
stress, gene functions associated with metal ion binding exhibited an increased
abundance per unit biomass in the tetraploid relative to the diploid (Figure 4-4; cluster
6). However, transcripts associated with tetrapyrrole biosynthesis and metabolism were
found to be initially more abundant per unit biomass in tetraploids, but this decreased
after 48 hours of drought treatment until abundance was near-parity between the two
ploidal levels (Figure 4-4; cluster 4). From the cellular perspective, transcripts inferred
to be involved in organic acid and carboxylic acid transport continually increased in
abundance in the tetraploid vs. the diploid over the duration of the drought treatment
(Figure 4-5; cluster 3). After the first 24 hours of drought, there was also a significant
enrichment of gene expression for metal ion binding, ADP binding, and salt stress
genes that were up-regulated per cell in the tetraploid, before dropping back to diploid-
like levels at the 48-hour mark (Figure 4-5; cluster 4+5).
Discussion
The climate is rapidly changing on a global scale, and given their lack of mobility,
plants in many locations are poised to experience novel abiotic stresses (Dai, 2012).
Tolmiea diplomenziesii and T. menziesii are both shade-loving plants that are restricted
to moist understory habitat (Baldwin et al., 2012; Visger et al., 2016), so we know little
about how these plants respond to drought conditions. Our goal here was to understand
the transcriptional response of Tolmiea to severe drought stress over time, and how that
response differs between the autotetraploid T. menziesii and its diploid progenitor T.
diplomenziesii.
Approximately 9.2% of all loci investigated were differentially responsive to
drought with respect to ploidal level in Tolmiea. Regulatory genes responsive to stress
response pathways are complex, often initiating substantial changes to global gene
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expression patterns. Abscisic acid (ABA), for example, is a well-documented signaling
hormone for drought response and can initiate a cascade of transcriptional change
across large swaths of the genome (reviewed in Wasilewska et al., 2008). During
drought stress in Triticum, ~48% of genes were differentially expressed in response to
increased stress-response hormone expression (Way et al., 2005). Based on these
findings in Triticum, abiotic stress treatments appear to induce shifts gene expression
across large portions of the transcriptome, which can influence the total transcriptome
size (the sum of transcription per cell). It is therefore critical that comparisons between
drought and non-drought treatments, even studies not involving ploidy comparisions,
avoid the classic assumption of equivalent transcriptome size, and instead implement
more recently developed methods that are independent of transcriptome size (Lovén et
al., 2012; Visger et al., submitted). In this study, we leverage newly developed methods,
and for the first time, simultaneously characterize drought-induced gene expression
divergence per transcriptome, per cell, and per biomass (Visger et al., submitted).
Below we discuss the major findings of these various analyses.
Variation in the transcriptomic response to drought stress
The tetraploid T. menziesii and the diploid T. diplomenziesii show a striking
difference in total transcriptome size throughout the drought stress treatment (Figure 4-
1). The diploid individuals show small differences in total transcriptome size, and those
change slightly in response to drought. However, the tetraploid individuals exhibit an
extreme degree of transcriptome size variability both between individuals and across
individual drought responses. The tetraploid individuals, either through higher allelic
dosage, higher heterozygosity, or a yet-unknown consequence of autopolyploidy,
appear more variable in transcriptome size than their diploid progenitors. It cannot be
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overstated that transcriptome size, across nearly all RNAseq studies, is normalized in a
way that assumes it to be invariable. Without the use of a spike-in-based methodology
such as the one we employed, these significant differences in total transcription
between the diploid and polyploid would have been ‘normalized’ out (see Loven et al.,
2012; Visger et al., submitted).
Within the principal component space of the 500 most variable loci, we found that
the diploid Tolmiea individuals all exhibited a similar magnitude of expression-level
response to drought at both the 24- and 48-hour marks (Figure 4-2), while tetraploid
individuals exhibited much greater variance in the magnitude of drought response, with
some individuals changing very little over 48 hours and others changing far more than
any of the diploids. Additionally, the gene expression of diploid individuals grouped by
population, while the tetraploids showed no population grouping. The absence of
grouping by population in the tetraploids is congruent with the hypothesis that they
arose from a single diploid population in the past (Soltis et al., 1989). However,
following an extreme bottleneck, such as originating from a single diploid population,
there is an expectation of reduced genetic, and thereby transcriptional variation, but
here the opposite is observed. One explanation is that autopolyploidy itself may
increase the plasticity of gene expression level. Diploids can harbor at most two alleles
per locus, while autotetraploids maintain four, and transcribing from four rather than two
alleles could provide a source of additional expression-level variability. In addition to
allelic dosage, autotetraploidy in Tolmiea was shown to increase heterozygosity, which
likely also contributes to the increased variability observed in this study (Soltis and
Rieseberg, 1986; Soltis and Soltis, 1989; Moody et al., 1993).
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Differential drought response (DDR)
The three different measures of DDR (Figures 4-3,4-4,4-5) show that some
clusters of genes (i.e., a group of genes that share a similar 2x vs. 4x differential
response profile) are more drought responsive in the diploid vs. the tetraploid.
However, none of the DDR clusters exhibiting a greater diploid response contained an
over-representation of any specific functional ontologies – implying that these clusters
are composed of genes drawn from across the transcriptome at random. This does not
suggest that there are no important drought-related genes in the more diploid
responsive clusters, because there could be, but that level of gene specificity is not yet
tractable in this study system. Instead, we focus on broad patterns of gene functions
associated with drought response. We observed a statistically significant
overabundance of some functional groups, both from a stoichiometric perspective
(Figure 4-3; cluster 3 and 6) and an abundance perspective (Figure 4-4; cluster 4 and 6,
Figure 4-5; cluster 3 and 4+5). Many of the enriched functional categories have been
previously documented as playing important roles in abiotic stress response. Some of
these functional groups, including processes related to ubiquitination, have been shown
to respond to drought via up-regulation (Stone, 2014). Other functional groups
identified here are typically down-regulated in response to drought, including ATP
synthase coupling factors and prenyltransferases (Tezara et al., 1999; Zhang et al.,
2008). The response directions of still other functional groups are context-dependent,
including oxidases responsible for maintaining redox homeostasis where the direction of
response is necessitated by the direction of redox imbalance. Below we discuss some
specific enriched functional groups and their possible implications for drought response
differences in Tolmiea.
89
Drought response pathways are typically categorized as abscisic acid-dependent
or abscisic acid-independent. In this study we find no enrichments of gene functional
categories associated with abscisic acid response (e.g., GO:0009737 – response to
abscisic acid, or more broadly GO:0009725 – response to hormone), indicating that the
drought expression response differences between T. diplomenziesii and T. menziesii
may be driven by other, non-ABA dependent mechanisms. It may be that diploid and
tetraploid Tolmiea do not differ in sensitivity to changing ABA concentration, or perhaps
they are differentially sensitive to ABA, but maintain different concentrations of the
hormone, which could be further complicated by cell size/density differences (see
Visger et al., submitted). ABA can also be accumulated by a decreased rate of ABA
degradation, which would not be detected using an RNAseq-based approach.
Unpublished and preliminary work by Visger and Patel attempted to identify if ABA
sensitivity differs between diploid and tetraploid Tolmiea by applying ABA in varying
concentrations (0, 1, 10, 100um) to leaf tissue and observing stomatal closure.
However, no concentration of ABA was found sufficient to induce stomatal closure in
either ploidy of Tolmiea. This might imply that either the abscisic acid was unable to
penetrate the cuticle, although our method of delivery has been effective in other
angiosperm systems (e.g., Franks and Farquhar, 2001), or that Tolmiea, a shade-loving
understory plant, has lost or greatly reduced its stomatal closure response to ABA. Zinc-
binding has previously been shown to be down-regulated in response to ABA, but here
neither species of Tolmiea decreased the expression of zinc-binding-related genes
relative to pre-drought conditions (Zschiesche et al., 2015). Instead, we observed that
both species increased the expression of the zinc-binding GO category 24 hours post-
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drought (Figure 4-5; cluster 4 and 5). The pattern of temporary increases in zinc
binding in response to drought is inconsistent with the expectations of ABA response,
lending further support to the idea that drought response differences in Tolmiea may be
ABA-independent. Additional and more rigorous work is required to tease apart
potential methodological insufficiencies of ABA application from physiological
interpretation.
Reactive oxygen species (ROS) are produced when plants are subjected to
abiotic stressors, including drought, and they have been shown to serve as important
signalers of abiotic stress (Jones, 2006; Foyer and Noctor, 2009; Choudhury et al.,
2013), with bursts of ROS production within the apoplast (an ROS signaling wave)
serving as a mechanism for intercellular stress signaling (see Mittler et al., 2011; Mittler
and Blumwald, 2015). Amine oxidase has been implicated as one of the major sources
of H2O2 production in the apoplast, and H2O2 has been shown to be one of the primary
intercellular ROS-based stress-signaling molecules (see Bolwell et al., 2002; Slesak et
al., 2007). Interestingly, in our experiment the DDR profile exhibiting a ‘burst’ in
expression level per cell during the first day of drought (Figure 4-5; cluster 4 and 5) is
enriched for amine oxidase activity. We see this spike in amine oxidase activity in both
the diploids and tetraploids, indicating that they respond similarly to drought stress,
although both the absolute abundance and the magnitude of change are greater in the
tetraploids. Given the difference in cell size and density between ploidal levels in
Tolmiea, intercellular signaling mechanisms driven by per-cell expression changes
could be altered quite significantly.
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The tetraploid with its higher amine oxidase expression per cell, and less dense
cells, might create a stronger ROS signaling wave, which would be more disruptive to
the redox balance than experienced by the diploid. One method of restoring redox
homeostasis is through the reduction of photosynthesis, a key producer of ROS (e.g.,
see Tripathy and Oelmüller, 2012), which unless scavenged can cause oxidative
damage. The per-cell photosynthetic output is inferred to be higher in the tetraploid (see
Chapter 2 and Chapter 3), and could drive both higher ROS production and elimination.
ROS homeostasis has been shown to differ by ploidal level, which plays a role in
differential drought response between diploid and autotetraploid Arabidopsis thaliana
(Del Pozo and Ramirez-Parra, 2014). The redox homeostasis resulting from higher
ROS production would be more susceptible to disruption by the increased ROS
production in response to drought stress.
Gene expression related to both general redox, including oxidoreductases and
coproporphyrinogen oxidases, as well as photosynthesis-related redox, is altered
substantially more in T. menziesii relative to T. diplomenziesii. Specifically, genes
associated with tetrapyrrole production, an important precursor of chlorophyll, are
reduced over time both per transcriptome and per biomass (Figure 4-3; cluster 3, Figure
4-4; cluster 4). Decreased chlorophyll production should result in lower photosynthesis
per cell, and a decrease in photosynthesis-generated ROS (photo-oxidation).
Additionally, the expression of genes related to the ATP synthase coupling factor F is
decreased much more in the tetraploid than in the diploid (Figure 4-3; cluster 3), which
has been shown to be a mechanism for avoiding photo-oxidative stress (Tezara et al.,
1999). Together these results suggest that tetraploid cells produce a larger ROS
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signaling wave in response to drought, disrupting redox homeostasis, prompting altered
gene expression of general redox pathways, as well as a specific reduction in
photosynthesis to limit ROS generation due to photo-oxidation.
Caveats and conclusions
An initially striking result of this study, showing transcriptional response to
drought, is its potential incongruence with previous results that suggested that T.
diplomenziesii is more drought-adapted than T. menziesii (Visger et al. 2016). That
previous work only found physiological differences in water-use efficiency across a
drought gradient, suggesting that the diploids may be better photosynthesizers during
long-term drought. However, the study presented here is limited to characterizing the
short-term response to extreme drought stress. Our results suggest that the tetraploids
may reduce their photosynthetic machinery in response to drought, which does not
conflict with the tetraploids’ lower photosynthetic water-use efficiency previously
observed relative to the diploid (Visger et al., 2016). Therefore, the results discussed
above should be considered as an addition to our current understanding of how drought
stress differentially impacts T. diplomenziesii and T. menziesii rather than a conflict.
It is also important to consider that this study is focused solely on the
transcriptional response to drought stress. It is possible that the increased
transcriptional drought response observed here is not being faithfully translated, nor can
it be assumed that the rate of translation is similar between ploidal levels. An almost
entirely overlooked step in the path from the genotype to phenotype change in
polyploids is the translation of mRNA to protein. The mRNA under active translation, the
translatome, serves as a middleman between the transcriptome and the proteome.
Genes that are up-regulated at the transcript level in the autopolyploid could go through
93
a bottleneck of sorts with respect to ribosome recruitment during translation, resulting in
non-differentially expressed protein. Translation is also not the last step on the path
from DNA to protein; and therefore, this same consideration can be applied to post-
translational modifications as well.
Finally, the plants used in this study represent a limited set of field-collected lines
that have been maintained within a common garden greenhouse at the University of
Florida for several years. Despite the use of a greenhouse, the climate of Florida is
quite different from that of the Pacific Northwest (PNW) where Tolmiea occurs naturally.
Accumulation of systemic stress tolerance in plants is an emerging field of research,
and findings suggest that plants exposed to repeated stressors develop a
tolerance(e.g., Mittler and Blumwald, 2015). No matter how carefully controlled the
environment, it is possible that the long-term exposure to differences in humidity and
temperature in our greenhouse relative to the PNW could alter the drought stress
response in this study.
In conclusion, we find that the tetraploid T. menziesii shows a much more
variable gene expression level response to drought than T. diplomenziesii, both at the
population level, as well as temporally. It is perhaps this higher degree of variability that
has enabled T. menziesii to inhabit a broader range relative to its diploid progenitor, and
our niche modeling predicts this disparity to increase in the future as the global
temperature change. Either due to post-transcriptional regulation, accumulated stress
tolerance, or a reduction in ABA sensitivity, we found no evidence for changes in ABA-
mediated gene expression responses to drought stress in Tolmiea. However, we did
find a number of gene functions differentially responsive to drought stress between
94
diploid and tetraploid Tolmiea that are directly related to ROS accumulation, ROS-
mediated stress signaling, and redox homeostasis, all critical components of drought
stress response that are either ABA-independent, or upstream of ABA in ABA-
dependent pathways. To our knowledge, this represents the second quantitative study
of gene expression response to drought stress between a diploid and autotetraploid
species pair – the other being a study in Arabidopsis thaliana which also found
substantial evidence for ROS and redox homeostasis differing between ploidal levels
(Del Pozo and Ramirez-Parra, 2014). While informative in a broad context, and a useful
first look into the gene expression changes following abiotic stress in natural
autopolyploid system, there is clearly a need to experimentally examine and test the
inferences drawn from these findings. Future work should focus on better clarifying the
role of ABA (or lack thereof) in Tolmiea, whether photosynthesis in the tetraploids
produces higher levels of ROS, and determine if the differential gene expression
discussed here is also accompanied by differential translation.
95
Figure 4-1. Bar plot of the total transcriptome size per cell (the sum of per cell normalized read counts). Bar colors represent treatment day, and are grouped by individual.
96
Figure 4-2. MDA plot of the 500 most variable loci. Diploids and autotetraploids are outlined in red and green respectively, and individuals coming from different populations are distinguished by shape. Dotted lines connect each day of treatment with the corresponding individual.
97
Figure 4-3. Six clusters of genes exhibiting a significantly different response per transcriptome to drought over time between T. diplomenziesii and T. menziesii. Significant functional enrichments are listed beside the clusters.
98
Figure 4-4. Six clusters of genes exhibiting a significantly different response per biomass to drought over time between T. diplomenziesii and T. menziesii. Significant functional enrichments are listed beside the clusters.
99
Figure 4-5. Six clusters of genes exhibiting a significantly different response per cell to drought over time between T. diplomenziesii and T. menziesii. Significant functional enrichments are listed beside the clusters, with cluster 4 and 5 combined due to overall similarity.
100
Figure 4-6. Venn diagram depicting the distribution of loci identified as responding differently to drought per transcriptome, per biomass, and per cell.
Per Cell
1191
Per Biomass
148
90
588
55
301
Per Transcriptome
122
n = 2,495
Differentially drought responsive genes by scale
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CHAPTER 5
GENERAL CONCLUSIONS
Although polyploidy has clearly experienced a major surge in research interest
over the last few decades, our understanding of this phenomenon remains patchy at
best. Often only the study of a single species (or species pair) serves as an illustration
for a specific phenomenon occurring at a certain time following polyploidization
(reviewed in Soltis et al., 2016). Autopolyploidy, which was long ago sidelined as
evolutionarily unimportant (e.g., Stebbins, 1950; Grant, 1981), has remained
understudied relative to allopolyploids. However, multiple studies have suggested that
autopolyploidy is as common as allopolyploidy in nature (Soltis et al., 2007; Barker et
al., 2016). Autopolyploidy is in some respects the more straightforward form of genome
doubling, as it results from a strict doubling of one genome. In contrast, allopolyploidy is
complicated by hybridization and the resulting subgenome interactions. Studying the
impact of autopolyploidy may therefore offer the clearer path towards understanding the
effects of genome doubling per se.
Throughout my dissertation work I applied an interdisciplinary approach towards
understanding the evolutionary impact of autopolyploidy in Tolmiea. Beginning with
Chapter 2, I combined field methods, ecological modeling, and measurements of
physiology to characterize divergence in climatic preference and water-use efficiency
between T. diplomenziesii and T. menziesii. Chapter 3 focused on developing methods
to overcome existing inadequacies in comparing gene expression change across ploidy
variation, enabling simultaneous comparisons of gene expression divergence across
three biological scales. Chapter 4 used the information gathered from Chapter 2, and
applied the methods from Chapter 3, to assess gene expression response to drought
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stress in T. diplomenziesii and T. menziesii. Below I summarize the major findings from
my dissertation and conclude with my future goals.
Glacial retreat can provide newly available range space, serving as a mechanism
for nascent polyploids to escape from minority cytotype exclusion (MCE; Levin, 1975).
In Tolmiea, the tetraploid occurs north of the diploid in regions that were previously
glaciated; however, it is unclear whether the tetraploid formed proceeding, or following,
the Last Glacial Maximum. Though it is not a consistent effect, some polyploids have
been found to be more cold tolerant than their diploid progenitors (e.g., Liu et al., 2011).
Increased cold tolerance may provide a nascent polyploid with a competitive advantage
over its diploid progenitor in the arctic or recently deglaciated regions. In other cases,
polyploids have been suggested to be more drought-tolerant than their diploid
progenitors, with multiple, both auto- and allopolyploids, which show an increased
drought tolerance compared to their parents (e.g., Li et al., 2013; Del Pozo and
Ramirez-Parra, 2014). Though like cold tolerance, increased drought tolerance is not
the case for all polyploids, and this trend is in need of additional empirical support.
Conversely, other researchers have suggested that even in absence of specific
physiological changes, the fixed-heterozygosity accompanying allopolyploidy conveys
an advantage by reducing the negative effects of inbreeding during colonization
(Brochmann et al., 2004).The non-fixed, but nonetheless increased, heterozygosity
maintained by many autopolyploids may also confer an adaptive advantage (Parisod
and Brochmann, 2010; Moody et al., 1993).
In Chapter 2, I used an ecological modeling-based approach to assess whether
diploid and autotetraploid Tolmiea differ in their abiotic niche preferences. Specifically,
103
the goal of this work was to determine how T. menziesii and T. diplomenziesii habitat
suitability differed with respect to temperature and/or precipitation. The results of
Chapter 2 do not point towards temperature variation as a key determinant of their
divergent niche space and instead suggest water availability might be more important.
This finding was empirically tested, showing that the two species differed in their
physiological response to decreased soil moisture. As noted above, other works have
found several polyploids to be better drought adapted than their diploid progenitors, but
in Tolmiea the reverse is observed. Diploid Tolmiea uses water more efficiently than the
tetraploid during drought conditions, which should enable the diploid to cope better with
long-term drought conditions (Visger et al., 2016). Stomatal guard cells are a critical
component of transpiration regulation, and as discussed in Chapter 2, Tolmiea is a rare
example of a system in which polyploidization does not increase stomatal guard cell
length or alter their density. This finding may help explain the observation that Tolmiea
deviates from the typical pattern of water-usage alterations observed in polyploids
compared to diploids.
To better understand differences in drought tolerance in Tolmiea, Chapter 4 used
the methods developed in Chapter 3 to investigate transcriptional change following
drought stress. Most significantly, the drought stress treatment revealed that the
tetraploid individuals are much more variable in their total transcriptome size relative to
the diploid progenitor. This trend in transcriptome size variation was shown within and
between populations, as well as across individual response to drought. The magnitude
of variability in tetraploid transcriptome size represents perhaps the most significant
result of the dissertation, and supports the idea that harboring additional alleles can
104
increase genetic variation. This result complements earlier findings of genetic variation
in autopolyploids compared to their parents using allozymes and other genetic markers
(e.g., Soltis and Rieseberg, 1986; Soltis and Soltis, 1989; Mahy et al., 2000). These
previous studies revealed that the increased allelic dosage and variability, as well as
increased heterozygosity, in autotetraploids can produce substantial genetic and
phenotypic variability (e.g., Tomekpe and Lumaret, 1991). A number of workers
considered these genetic attributes the key to the success of autopolyploids (e.g., Levin
1983; Thompson and Lumaret, 1992).
The genes responsive to drought tended to be expressed more highly in the
tetraploid T. menziesii than the diploid, which is in line with previous comparisons of
diploid and autotetraploid drought response (e.g., Del Pozo and Ramirez-Parra, 2014).
Significantly, gene functional categories related to reactive oxygen species (ROS) and
redox homeostasis were over-represented among the drought responsive genes that
were more highly expressed in the tetraploid. Photosynthesis is a key source of ROS,
which unless eliminated (scavenged) can also cause oxidative damage (e.g., see
Tripathy and Oelmuller, 2012). ROS are also produced when plants are subjected to
abiotic stressors, including drought, and have been shown to serve as important stress
signaling molecules (Choudhury et al., 2013; Jones, 2006; Foyer and Noctor, 2009).
During drought conditions, the combination of higher and less water-efficient
photosynthesis per cell in the tetraploid compared to the diploid (see Chapter 2 and
Chapter 3) may result in accumulation of more ROS. ROS serving as abiotic stress
signaling molecules means that a higher accumulation of ROS per cell could explain the
105
increased transcriptional response to drought observed in the tetraploid compared to
the diploid.
When considering the conclusions drawn from gene expression data, it is
important to remember that mRNA abundance does not directly impact phenotype.
The central dogma of biology is the path of DNA to protein, of which transcription is an
early step. Perhaps the increased allelic dosage and allelic variability in autotetraploid
Tolmiea drives an increase in gene transcription. However, the excess transcripts must
ultimately be translated into protein to provide a functional response to drought.
Although translation of mRNA is a key intermediate step along the path from the
genotype to phenotype, it has been overlooked regarding its importance in
understanding gene dosage effects following polyploidy. The sum of mRNA under
active translation, the translatome, is ripe for study, as it has all of the advantages of
transcript-based analysis, the ability to parse homeologous contributions that have
diverged only at synonymous sites (in the case of allopolyploids), while also being one
step closer to the phenotype. Further, the translatome is one of the most likely places
to begin exploring the discontinuity between the transcriptome and proteome (Vogel and
Marcotte, 2012). Future studies should ask if genes up-regulated at the transcriptome
level in the tetraploid might be bottlenecked by translational throughput (e.g., ribosome
recruitment during translation), which therefore impact downstream protein levels that
directly effect phenotype. For example, are relatively more abundant transcripts in the
polyploid being translated at a lower rate, in other words, ribosome-limited or under
translational compensation? Polyploidy often results in larger cells and thereby a higher
volume of cytosol; could this lead to a larger pool of ribosomes from which to draw? If
106
polyploid cells hold a larger pool of ribosomes, this could lead to higher rates of
translation of otherwise equivalently expressed transcripts resulting in diploid-like
transcript expression level but higher protein abundance.
Ribosome profiling offers a high-throughput method for assaying both ribosome
density and position along each mRNA in the translatome (Ingolia et al., 2009; Ingolia,
2014). The ribosome profiling workflow involves immobilization of ribosome complexes,
followed by digestion of non-ribosome-bound RNAs. This yields a pool of ~30-bp
mRNAs representing the ribosome ‘footprint’ and is readily applicable to typical
differential expression workflows or in this case, differential translation—and is
compatible with the methods developed in Chapter 3. Because ribosome profiling
begins with total RNA, it is possible to subsample a single RNA extraction for mRNA
sequencing and ribosome ‘footprint’ sequencing. This allows for a paired study design,
which can compare the transcriptome to the translatome at a single point in time, from
exactly the same tissue sample, for each individual. Future work in Tolmiea will be
aimed at investigating whether increases in polyploid gene expression are accompanied
by increases in translational throughput and the patterns of this interaction across three
biological scales.
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APPENDIX A BIOCLIMATIC RESPONSE VARIABLES FROM CHAPTER 2
Figure A-1. Niche suitability response to eight bioclimatic variables (Bio 2, 5, 8, 11, 15, 16, 17, and 18) generated during niche modeling of T. diplomenziesii (left) and T. menziesii (right).
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APPENDIX B ENVIRONMENTAL SPACE PCA FROM CHAPTER 2
Figure B-1. Environmental space PCA generated using ecospat (Broennimann et al., 2014) for testing niche equivalence and similarity.
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BIOGRAPHICAL SKETCH
Clayton J. Visger grew up in Sacramento, California, where he attended the
Leonardo da Vinci School from first to eight grade, and graduated from John F.
Kennedy High School in 2003. He was a member of the John F. Kennedy wrestling
team, and served as the team’s captain. After high school, he enlisted in the Air Force
and served four years as a Loadmaster. During his military service, he began taking
online courses through Solano Community College, and found an appreciation for
classes with a focus on biology. Following his time in the Air Force, he enrolled in
California State University, Sacramento, and began taking classes in the spring of 2008.
During his freshman and sophomore year he was a member of the Sacramento State
Rugby team, before choosing to focus all of his energy on academics and research with
his mentor Dr. Shannon L. Datwyler; he graduated magna cum laude in the spring of
2012. Under the direction of Drs. Douglas E. Soltis and Pamela S. Soltis, he began his
doctoral work at the University of Florida in the fall of 2012, and graduated in the spring
of 2017. In August 2017, Clayton Visger began an appointment as an Assistant
Professor in the Department of Biological Sciences at his alma mater, California State
University, Sacramento.