Download Conifer genomics and adaptation: at the crossroads of genetic

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Conifer genomics and adaptation: at the
crossroads of genetic diversity and genome
function
Author for correspondence:
John J. MacKay
Tel: +44 (0)1865 275088
Email: [email protected]
Julien Prunier1, Jukka-Pekka Verta2 and John J. MacKay1,3
Received: 13 April 2015
Accepted: 14 June 2015
Department of Plant Sciences, University of Oxford, Oxford, OX1 3RB, UK
1
Centre for Forest Research and Institute for Systems and Integrative Biology, Universite Laval, Quebec, QC G1V 0A6, Canada;
2
Friedrich Miescher Laboratory of the Max Planck Society, Spemannstrasse 39, T€
ubingen 72076, Germany; 3Present address:
Contents
Summary
44
I.
Introduction
44
II.
Why conifer genomics?
45
III.
Conifer genome structure and evolution
46
V.
Emerging research directions toward a more integrated
understanding of genetic diversity and genome function
VI. Conclusion
IV. Genomics of adaptation in conifers
57
58
Acknowledgements
58
References
58
49
Summary
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doi: 10.1111/nph.13565
Key words: Coniferales, gene expression,
genome evolution, genomics of adaptation,
next generation sequencing (NGS),
RNA-sequencing, speciation-by-adaptation.
Conifers have been understudied at the genomic level despite their worldwide ecological and
economic importance but the situation is rapidly changing with the development of next
generation sequencing (NGS) technologies. With NGS, genomics research has simultaneously
gained in speed, magnitude and scope. In just a few years, genomes of 20–24 gigabases have
been sequenced for several conifers, with several others expected in the near future. Biological
insights have resulted from recent sequencing initiatives as well as genetic mapping, gene
expression profiling and gene discovery research over nearly two decades. We review the
knowledge arising from conifer genomics research emphasizing genome evolution and the
genomic basis of adaptation, and outline emerging questions and knowledge gaps. We discuss
future directions in three areas with potential inputs from NGS technologies: the evolutionary
impacts of adaptation in conifers based on the adaptation-by-speciation model; the contributions of genetic variability of gene expression in adaptation; and the development of a broader
understanding of genetic diversity and its impacts on genome function. These research directions
promise to sustain research aimed at addressing the emerging challenges of adaptation that face
conifer trees.
I. Introduction
Genomics research has gained in speed, magnitude and scope
simultaneously as a result of technology developments and, most
recently, next generation sequencing (NGS) technologies that
facilitate large-scale analysis with unprecedented data production
capacity. The potential to analyze whole genomes of thousands of
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individuals in model plants and animals is nothing less than
revolutionary. The impacts are also felt in nonmodel systems such
as conifer trees. In just a few years, NGS has enabled the sequencing
of several conifer genomes despite their very large size, estimated at
20–24 Gbp (Birol et al., 2013; Nystedt et al., 2013; Neale et al.,
2014), with several others expected in the near future. Because of
the massive size of conifer genomes, sequencing and analyses
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remain massive undertakings even though methods and capacity
are being developed to overcome the inherent challenges.
Recent genome sequencing initiatives and cDNA sequence
resources developed over nearly two decades have led to insights
into conifer biology, genetics and genome evolution. Here, we
begin by summarizing the major scientific and societal arguments
which motivate conifer genomics research. We then review three
major areas of research at the center of recent developments in
conifer genomics. First, the current understanding of conifer
genome structure and evolution is reviewed in light of recent
analyses examining the forces that have shaped these megagenomes and the functional implications at the level of gene coding
content. Second, we review progress and perspectives toward
understanding the genomics of adaptation, exploring two research
themes: (1) the genomic basis of adaptation in which we use the
speciation-by-adaptation model as a conceptual framework; and
(2) the role of genetic variability of gene expression. Third, we
discuss emerging research directions which are expected to
contribute to a more integrated understanding of genetic diversity
and genome function.
II. Why conifer genomics?
1. Taxonomic position, ecological and economic importance
of conifers
The conifers (Pinophyta or Coniferales) are one of four living
groups of gymnosperms, which together with the extant flowering
plants or angiosperms (Magnoliophyta) make up the seed plants
(Raven et al., 2005; Gernandt et al., 2011). There are fewer than
1000 species of extant gymnosperms including 635 recognized
conifer species (www.catalogueoflife.org/, Farjon & Page, 1999),
compared to 250 000 angiosperms species (Kuzoff & Gasser,
2000) resulting from tremendous and more recent adaptive
radiation (Kenrick, 1999).
Despite their relatively small numbers, conifer species are
distributed in a variety of habitats from evergreen subtropical
forests (e.g. Pinus krempfii in Vietnam; Wang et al., 2014), to boreal
forests in the Northern hemisphere (e.g. Picea mariana in Canada;
Farrar, 1995), and from sea level (e.g. Pinus pinaster in Western
Europe; Burban & Petit, 2003) to high mountainous locations (e.g.
Picea mexicana in Mexico; Ledig et al., 1997). Many conifers are
widely distributed and have large ecological amplitudes (e.g. Pinus
sylvestris found from Western Europe to Asia at elevations ranging
from sea level to 2440 m altitude; Castro et al., 2004) but some of
them are more widely dispersed and occur in small areas with
uniform environmental conditions (e.g. Picea chiuauana limited to
western mountain tops in Mexico; Ledig et al., 1997). Conifers play
an important role in global carbon, nutrient and atmospheric
cycles, provide many ecosystem services and are widely used in
reforestation programs owing to their adaptability and dominance,
especially in temperate and boreal forest ecosystems.
Conifers have gained significant economic importance worldwide because of their amenability to large-scale plantations to
produce wood, their potential for rapid growth, and their relative
ease of processing to make both paper and solid wood products.
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Over the last several decades, genetic selection and breeding
programs have been implemented in both the Northern and
Southern hemispheres for pines (Pinus spp.), spruces (Picea spp.),
Douglas-fir (Pseudostuga menzeii), larches (Larix spp.), Japanese
cypress (Cryptomeria japonica) and firs (Abies spp.), among others.
Conifers represent the longest lived individuals on Earth with
maximum lifespans of a few thousand years for several species
(Loehle, 1987), suggesting that their success is underpinned by
considerable phenotypic plasticity and resistance to biodegrading
processes.
2. Contemporary issues and emerging challenges
The health and adaptation of forest trees populations will become
increasingly challenged by environmental changes expected before
the end of the 21st Century (Alberto et al., 2013). Simulations
indicate that up to 60% of tree species in boreal and temperate
regions will have a hard time adjusting to warmer climates
predicted for 2085 (Hamann & Wang, 2006) and conifers may be
among the most negatively affected (Hanewinkel et al., 2013).
Aitken et al. (2008) outlined the three possible outcomes for forest
tree populations under present climate warming scenarios: adaptation, migration or extirpation. Of these three major scenarios, the
adaptation potential is the most complex to ascertain and will
depend upon phenotypic variation and standing genetic variation
(Siol et al., 2010), strength of selection, fecundity and biotic
interactions (Aitken et al., 2008). In recent years, niche modeling
has developed that integrates phenotypic or genetic data, site
conditions and climate models, and has been applied to conifers in
Europe and North America to project future species distributions
in the face of climate change (Rehfeldt et al., 2014). These analyses
indicate that inclusion of genetic variability as well as phenotypic
plasticity data influences the outcomes of species distribution
modeling (Benito et al., 2011), and that some areas will contain
genotypes becoming less and less adapted to the climate (Rehfeldt
et al., 2014).Therefore, understanding which part of standing
genetic variation is adaptive as opposed to neutral is a central
research theme in evolutionary biology and was identified as a
major challenge to address for forest tree genomics (Neale &
Kremer, 2011).
The first decade of the 21st Century has already provided us with
striking examples of biotic outbreaks resulting from climate
change or globalization with devastating effects on conifers. For
example, the mountain pine beetle has decimated tens of
thousands of hectares of pine forest in western North America
because of temperature-driven range expansion of this insect (Raffa
et al., 2013). Meanwhile, plant pathogens such as Phythopthora
spp. have moved around the world with globalization and in some
cases have jumped to new hosts. In 2009, P. ramorum was reported
to infect larch plantations on a large scale in the UK but was
previously known to infect hardwood trees and cause sudden oak
death (Brasier & Webber, 2010). Developing an understanding of
the evolution and adaptability of biotic threats, conifer tree
defences and their interactions represents a rapidly growing
challenge which may at least in part be addressed by genomics
research.
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A shift of conifer genomics research to these issues and challenges
entails a better understanding of adaptation to climate, phenotypic
plasticity and drivers of evolution.
III. Conifer genome structure and evolution
Genome sizes have been estimated from 18 Gbp to over 35 Gbp
for major conifers (Murray et al., 2012) making them on average
2009, 249 and 109 the size of the genomes of Arabidopsis,
poplar and maize, respectively. The very large size of conifer
genomes has played a major role in shaping both the
research questions and the experimental approaches in conifer
genomics.
1. Conifer genome structure
Gene discovery Large-scale EST and cDNA sequencing have
occupied a central role in conifer genomics owing to the large size of
conifer genomes and enabled the development of databases
(Fernandez-Pozo et al., 2011), transcriptome profiling, and efficient genotyping platforms, among others (Neale & Kremer, 2011;
Mackay et al., 2012; Ritland, 2012). Conifers have been shown to
share a majority of expressed genes with angiosperm plants, thereby
facilitating comparative analyses, but 30–40% of their genes do not
match proteins of known function (Kirst et al., 2003; Rigault et al.,
2011). Genotyping platforms have been derived from cDNA
sequencing and have led to the construction of genetic maps of
higher density which have enabled structural (Eckert et al., 2009b;
Pelgas et al., 2011; Chancerel et al., 2013; Neves et al., 2014) and
comparative genomics analyses (Komulainen et al., 2003; Pavy
et al., 2012b). More recently, gene sequence analysis from higher
throughput pyrosequencing (Parchman et al., 2010) and RNA
sequencing (RNA-seq) has enabled the identification of sequence
variations, most prominently single nucleotide polymorphisms
(SNPs) and gene expression levels, although de novo transcriptome
analysis from short reads still poses assembly challenges (Chen
et al., 2012; Wang et al., 2013; Yeaman et al., 2014).
Genome sequencing Whole-genome sequencing was recently
reported for Norway spruce (Nystedt et al., 2013), white spruce
(Birol et al., 2013), loblolly pine (Neale et al., 2014); assemblies
were released for sugar pine and Douglas-fir (http://pinegenome.org/pinerefseq/) and reduced-depth sequencing was
reported for several other species within the coniferales (Nystedt
et al., 2013). These developments have been driven by progress in
shotgun genome sequencing and associated bioinformatics methods (Simpson et al., 2009; Nystedt et al., 2013; Zimin et al., 2013,
2014) which have been applied to analyzing both haploid and
diploid conifer DNA. The sequences and assemblies are shedding
new light onto conifer genome evolution (Soltis & Soltis, 2013; De
La Torre et al., 2014); however, assemblies reported to date are
highly fragmented and each comprises > 10 million unordered
scaffolds. The N50 scaffold sizes (i.e. length of the shortest scaffold
among those that collectively cover 50% of the assembly) are
between 6 and 67 kbp. Future developments aimed at turning these
assemblies into contiguous genomes will need to increase scaffold
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size by about two orders of magnitude and position the scaffolds
along chromosomes, forming pseudo molecules. These goals may
be achieved through a combination of improved libraries (e.g.
mate-pair libraries), longer reads (e.g. single molecule sequencing),
improved computational analyses, more efficient and cost-effective
physical mapping methods (e.g. optical mapping) and integration
of high-density genetic maps (expanding on the methods described
in Pelgas et al., 2011; Neves et al., 2014).
Retrotransposons underpin the large size of conifer
genomes The accumulation of long terminal repeat retrotransposons (LTR-RT) was assumed to account for the very large sizes of
conifer genomes, and this was confirmed by recent genome
sequence analyses. In total, LTR-RT sequences were estimated to
represent 58% of the genome both in Picea abies and the Pinus taeda
based on sequence homology to known elements (Nystedt et al.,
2013; Neale et al., 2014; Wegrzyn et al., 2014). Estimates for other
members of the Pinaceae were in the 40–50% range (Nystedt et al.,
2013), which may be lower due to shallower sequencing. The total
representation of all transposable element (TE) classes was
estimated as 69% in P. abies (Nystedt et al., 2013) and 80% in
P. taeda (Wegrzyn et al., 2014), depending in part on the methods
of identification. Some of the individual LTR-RT sequences were
estimated to have tens to hundreds of thousands of copies in the
genome (Morse et al., 2009; Raherison et al., 2012). Angiosperm
genome size and structure are also largely influenced by LTR-RTs
but their representation in the genome is highly variable between
species (Bennetzen et al., 2005; Michael, 2014). Conifer genomes
have very low content of type II transposable elements and LINEs
compared to most angiosperms (Nystedt et al., 2013; Sena et al.,
2014).
The abundant conifer LTR-RTs have features that are uniquely
representative of conifers. Only three families, the Ty3/Gypsy,
Ty1/Copia and Gymny superfamilies make up the bulk of LTRRTs in conifers as shown by recent genome annotations (Nystedt
et al., 2013; Neale et al., 2014; Wegrzyn et al., 2014) and BAC
sequencing (Morse et al., 2009; Kovach et al., 2010; Sena et al.,
2014). The comparative analysis of LTR-RTs in conifers by
Nystedt et al. (2013) came to three main conclusions: (1) Ty3/
Gypsy and Ty1/Copia showed high levels of expansion and
diversity, exceeding those observed in any of the angiosperms; (2)
The lack of removal of replicated LTR-RTs is responsible for their
massive accumulation rather than a higher rate of multiplication;
(3) The accumulation of retro-elements in conifer genomes is very
ancient and has occurred over a very long timeframe spanning
tens to hundreds of millions of years, in contrast to LTR-RT-rich
monocot genomes (e.g. rice, maize) where multiplication has
been concentrated in the last few millions of years. In line with
this time frame, much of the diversification of LTR-RTs is
estimated to have occurred after the divergence of genera within
the conifers.
Although plant retrotransposons tend to be more abundant in
regions of the genome that are gene-poor, an analysis of Arabidopsis
thaliana showed that 7.4% of genes contained TE fragments
(Lockton & Gaut, 2009). Sena et al. (2014) reported that traces of
TE could be found in some conifer genes but their occurrence in
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genes has not been examined at the level of the whole genome and
their impacts on genes has not been studied in any detail.
Abundant gene-like fragments, pseudogenes and gene duplications Gene duplication plays a central role in genome evolution
and it is generally accepted that most duplicates are removed from
the genome or remain as nonfunctional pseudogenes. Conifers may
be inefficient at eliminating nonfunctional gene copies, as observed
with the accumulation of LTR-RTs, leading to the accumulation of
derelict gene copies and gene-like sequences. Conifer genome
annotations have revealed a surprisingly large fraction of sequences
classified as genes or gene-like fragments. Gene-like sequences
represented 2.4% and 2.9% of the P. abies and P. taeda genome,
respectively, (Nystedt et al., 2013; Neale et al., 2014) and as high as
4% from earlier analyses (Morgante & De Paoli, 2011). These
estimates would represent 500–800 Mbp of DNA and seem
incompatible with the number of functional genes which are
estimated between 50 000 and 59 000 by the same authors
(Nystedt et al., 2013; Neale et al., 2014). This apparent discrepancy
may be explained by the abundance of pseudogenes reported in
conifers (Kovach et al., 2010); however, a large-scale characterization of pseudogenes is lacking and this hypothesis has not been
directly tested at the genome level. Pseudogene frequency was
surveyed among secondary metabolism gene families in the white
spruce genome and reported to be abundant but variable in
numbers depending on the pathway (Warren et al., 2015).
Understanding the origin, accumulation and fate of functional
duplicated genes is a key to unraveling genome function and
evolution, but will likely be complicated by the pseudogenes found
in conifers until significantly more contiguous genome sequences
become available.
2. Genome evolution and speciation
The split between angiosperms and the gymnosperms dates back c.
325 Myr (Bowe et al., 2000). Gymnosperms have significantly
slower evolution rates and less dynamic radiation than flowering
plants (Leitch & Leitch, 2012). Early insights into the composition
and structure of conifer genomes indicated that they are different
from angiosperms and that their organization could not readily be
predicted or deduced from lessons learned in other plant genomes
(Mackay et al., 2012).
Comparative evolution of genome size and structure Gymnosperm genomes are larger on average and less variable in size
compared to angiosperm genomes (Leitch & Leitch, 2012). The
extent of variation in genome size was estimated at 16-fold among
the gymnosperms and 2400-fold among the angiosperms (Bennett
& Leitch, 2005). The modal and mean genome size are
1C = 10.0 pg and 1C = 18.8 pg, respectively, in gymnosperms,
compared to just 1C = 0.6 and 5.9 pg in angiosperms (Leitch &
Leitch, 2012). Within the gymnosperms, the coniferales stand out
as having the largest average genome size (Murray et al., 2012).
The number of chromosomes, the size of individual chromosomes and ploidy are remarkably less variable in gymnosperms and
particularly in conifers compared to angiosperms. Conifers have
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large chromosomes and their 1n count varies from 9 to 33; nearly all
members of the pine family (Pinaceae) have 12 chromosomes. In
contrast, angiosperms have 1n = 2 to 320. In addition, genetic
mapping showed a high level of synteny in gene order within and
across major genera in the Pinaceae (Pavy et al., 2012b), consistent
with their low level of variation in chromosome number. There is
no evidence of recent whole-genome duplication in conifers despite
their large genomes (Kovach et al., 2010; Nystedt et al., 2013),
whereas angiosperm genomes have been shaped by several wholegenome duplications (Bennetzen et al., 2005; Michael, 2014).
Leitch & Leitch (2012) concluded that chromosome structure is
less dynamic in gymnosperms than angiosperms as a result of both
ecological and genomic factors that have acted to favor slow
genome evolution in gymnosperms.
Ancient and highly conserved gene content Different lines of
evidence reveal that the gene content of contemporary conifer
genomes is ancient and largely conserved between species. The
ancient origin of genes and gene families was in the pine family
shown by the much larger fraction of the gene duplication events
that pre-date the angiosperm–gymnosperm split (c. 300 Myr ago
(Ma)) compared to those that occurred more recently (Pavy et al.,
2012b). In regard to gene structure, conifer genes tend to
accumulate long introns, with the largest introns surpassing 60 kb
in spruce (Nystedt et al., 2013) and 120 kb in pine (Wegrzyn et al.,
2014). In general, one or a few long introns of several kb are found
in conifer genes and conserved across species (Sena et al., 2014),
although most introns are 100–200 bp and comparable in size to
angiosperm introns (Sena et al., 2014; Wegrzyn et al., 2014). It
might be expected that very long introns in conifers would reduce
the level of gene expression – although no evidence has been found
to support this hypothesis (Nystedt et al., 2013; Sena et al., 2014) –
in contrast to mammalian genes where high expression tends to be
associated with shorter introns (Castillo-Davis et al., 2002).
Evolution of gene families and functional consequences The
evolution of genes and gene families is a fundamental mechanism
for phenotypic change and adaptation. The rapid evolution of gene
families in angiosperms has been a major facilitator of radiation and
adaptation (Leitch & Leitch, 2012). In comparison, gene families
evolve more slowly in conifers (Pavy et al., 2012b); for example,
transcription factors in particular are much less numerous in
conifers (Rigault et al., 2011). Hence, although gene families
largely overlap between conifers and angiosperms (Rigault et al.,
2011; Wegrzyn et al., 2014), entire gene clades are sometimes not
detected and differential patterns of expansion are observed within
some clades (reviewed in MacKay & Dean, 2011; Mackay et al.,
2012). As a result, inferring orthology relationships based on
sequence alone is nearly impossible in large gene families. This has
been observed for regulatory genes such as KNOX-I genes (GuilletClaude et al., 2004) and MADS box genes (Gramzow et al., 2014),
among others. Several classes of enzymes also occur in gene families
of moderate to large size in plants and are involved in conifer
defenses, including beta glucosidases (Mageroy et al., 2015) and
terpene synthases (Keeling et al., 2011), and in growth such as
cellulose synthases (Nairn & Haselkorn, 2005).
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One approach that facilitates insight into the biological role of
conifer genes is to integrate results from large-scale sequence
analyses and transcriptome profiling. We illustrate this approach in
the NAC-domain gene family where attempts have been made to
identify family members linked to vascular development and
woody growth (Duval et al., 2014) (see Fig. 1). The Picea glauca
gene PgNAC-7 (Fig. 1a) was identified as a functional ortholog of
the Arabidopsis thaliana ATSND1, VND6/7 genes that regulated
secondary cell wall formation (Demura & Fukuda, 2007);
however, functional data are lacking for the other 20+ known
NACs in P. glauca. Here, we integrated phylogenetic and transcript
data to identify PgNAC-8 as a candidate ortholog of AtSND2, three
genes which regulate complex carbohydrates during secondary cell
wall formation (Zhong et al., 2010) (Fig. 1). Four other Pg Picea
glauca NAC-domain sequences are closely related (Fig. 1a) but only
PgNAC-8 is preferentially expressed in secondary xylem (Fig. 1b).
The transcript profiling data combined with phylogenetic tree also
identifies potential cases of neo- or sub-functionalization following
gene duplication events (Fig. 1b; see *, to the right).
Slow species evolution despite habitat diversity Conifers occupy
a high diversity of ecological niches and are dominant in many
temperate and boreal ecosystems; therefore, a broader diversity of
species might be expected than is observed in the conifer phylum.
Compared to angiosperms, conifers evolve more slowly (Leitch &
(a)
Leitch, 2012) and have a synonymous mutation rate that is
15-fold lower (Buschiazzo et al., 2012). The slow evolution of
conifers is also indicated by the abundance of natural or
domestic hybridization events, for example in North American
pines, indicating incomplete reproductive isolation between
species.
In contrast to their relatively low species diversity, conifers are
characterized by high levels of heterozygosity and intraspecific
diversity. These are imputable to their large population sizes,
outbreeding mating systems and efficient gene flow due to windpollination, which in turn maintain common alleles over large
geographical distances and low levels of genetic differentiation
between populations (Achere et al., 2005; Namroud et al., 2008;
Holliday et al., 2011; Prunier et al., 2012; Alberto et al., 2013). In
addition, conifers have particularly low average linkage disequilibrium (LD) (Brown et al., 2004; Pavy et al., 2012a), which is often
confined within genes (Namroud et al., 2010). The impacts of LD
are also influenced by the large physical distances separating genes
as shown by BACs analyses (Kovach et al., 2010; Magbanua et al.,
2011; Sena et al., 2014). Taken together, these features lead to
largely independent evolution of individual genes (Savolainen
et al., 2007). They also favor efficient natural selection (compared
to drift) in species with large populations leading to the establishment of clines of locally adapted genotypes (Aitken et al., 2008;
Alberto et al., 2013).
(b)
Fig. 1 Integrated analysis of gene family
phylogeny and transcriptome profiling data in
the NAC-domain gene family. (a)
Phylogenetic tree of NAC-domain genes of
known sequences from Picea glauca and
selected Arabidopsis thaliana NACs ATSND1
and VND6/7 (in shaded box) that regulate
secondary cell wall formation in vessels and
fibers, constructed by Duval et al. (2014). (b)
Transcript abundance classification data
(heatmap) from the PiceaGenExpress
database (Raherison et al., 2012). Columns
represent vegetative tissues: B, vegetative
buds; F, foliage; XM, xylem – mature; XJ,
xylem – juvenile; P, phelloderm; R,
adventitious roots; M, megagametophytes; E,
embryogenic cells; transcript levels represent
relative abundance classes within each tissue,
grey is for missing data. *, Pairs of close
paralogs with clearly differentiated gene
expression indicative of functional divergence.
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IV. Genomics of adaptation in conifers
The genomic basis of adaptation has been investigated in several
tree species including conifers. Studies have largely focused on
identifying intraspecific DNA sequence variations that are linked to
adaption (Neale & Kremer, 2011). Researchers have investigated
the genetic architecture of adaptive traits such as phenology, cold
acclimation and resistance to aridity, and described patterns of
variation that may indicate the effects of evolutionary forces at the
population level (Savolainen et al., 2007). Here, we first discuss the
concept of speciation-by-adaptation as a framework to develop
further evolutionary insights into the genomics of adaptation in
conifers and, second, examine opportunities to gain a more
functional understanding through investigations of gene expression
variability. In both cases, we highlight opportunities to use NGS
technologies to accelerate discoveries.
1. Genomics architecture and population genetics of
adaptation
Speciation-by-adaptation – a framework for genomics of adaptation A speciation-by-adaptation model has been developed
from work integrating natural selection and genetic divergence with
analyses of the genomic distributions of loci contributing to
adaptive trait variations and reproductive barriers. The speciationby-adaptation model outlined by Turner et al. (2005) has been
widely discussed and tested in a variety of organisms (Jones &
Wang, 2012; Nosil, 2012; Renaut et al., 2013; Nadeau et al., 2014)
but not in conifers. The model proposes that a locus undergoing
divergent natural selection between two populations draws adjacent loci into high genetic divergence through hitchhiking which,
in turn, could lead to reproductive isolation and speciation. Using
an island/ocean metaphor to compare two populations settled in
different environmental conditions, genomic regions that encompass polymorphisms undergoing divergent selection are expected to
display between-population genetic differentiation levels superior
(the ‘islands’) to the general neutral differentiation level of the
whole genome (the ‘sea level’) (Nosil et al., 2009) (see Fig. 2, three
left side panels). These islands-of-divergence could ultimately
encompass polymorphisms leading to reproductive isolation
between the two populations (Fig. 2c) (Nosil et al., 2009). For
instance, a gene allele that is positively selected in drier conditions
may be in genetic linkage to another gene allele affecting pollen
phenology. As a consequence of hitchhiking under dry conditions,
pollen production time may be delayed and populations growing in
dry and wet conditions may consequently become reproductively
isolated. Described as extrinsic reproductive isolation (Nosil &
Schluter, 2011), this model plainly describes at the genome level
how adaptation-with-gene-flow can lead to speciation (Butlin et al.,
2012; Yeaman, 2013).
In this section (IV.1. ‘Genomics architecture and population
genetics of adaptation’), we review the state of knowledge on
adaptation in conifers and its genomic basis, providing additional
insights as to how the speciation-by-adaption model fits with
conifers considering their unique genome features, their population genetics and patterns of speciation.
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Adaptation at the phenotypic level For over 50 yr, adaptation has
been studied in conifers at the phenotypic level by using pedigrees
and provenances in reciprocal transplant experiments or common
garden tests. Variation has thus been demonstrated in several
adaptive traits such as the response to cold (Morgenstern, 1969),
among others. Genetic determinism has been established for many
traits by estimating the heritable component of phenotypic
variation (i.e. heritability, h²) or quantitative genetic differentiation
between populations (QST). Results indicate that heritable genetic
variations modulate phenotypes in response to selective factors
(Savolainen et al., 2007; Prunier et al., 2011) and imply that
adaptive variations are imprinted in conifer genomes. Phenotypic
plasticity is also observed and each genotype may present a more or
less characteristic reaction norm according to the site conditions.
Given the distribution of adaptive trait variation among populations and families, quantitative genetics theory predicts that they
are underpinned by many alleles and loci with small genetic effects,
such that low levels of genetic differentiation are expected for the
DNA polymorphisms involved (Le Corre & Kremer, 2012).
Nevertheless, several polymorphisms harboring FST values significantly higher than expected for a pure neutral model have been
identified in conifers.
Molecular studies of adaptation More recently, genomes and
population genetics have been studied in several conifers with
emerging molecular marker tools being developed to accelerate
breeding (Aitken et al., 2008; Neale & Kremer, 2011). Many of
these studies have focused on the identification of genetic variations
involved in climate adaptation to help breeding programs adjust to
changing climate conditions. SNP-arrays represent the most widely
used technology because they facilitate the screening of large
number of SNPs and genes (Namroud et al., 2008; Eckert et al.,
2009a; Pavy et al., 2013). Experiments designed to detect SNPs
involved in adaptation can be separated into two categories: (1)
‘bottom-up’ analyses that study genetic variation in species or
populations distributed across different environments; and (2)
‘top-down’ analyses that aim to identify polymorphisms associated
with the variation of a known adaptive trait (Storz, 2005). On the
one hand, bottom-up analyses such as FST-based outlier detection
are particularly well suited for studying adaptation in conifers
because they assume that loci are independent and that hierarchical
population genetic structure has been absent since the beginning of
their development (Beaumont & Nichols, 1996). This close match
between bottom-up analysis assumptions and conifer population
properties may explain the success in identifying adaptive
polymorphisms in conifers despite the very low levels of differentiation at adaptive loci that is predicted from quantitative genetics
theory (Eveno et al., 2008; Prunier et al., 2011). On the other hand,
top-down analyses are consistent with the predictions of quantitative genetics. For example, association genetics studies of adaptive
traits such as budset phenology for temperature adaptation have
usually found many SNPs involved in trait variation, each typically
having a small effect (c. 1–5%) (Eckert et al., 2009a). Researchers
usually deploy complementary approaches to confirm findings;
therefore, a significant part of the detected polymorphisms are
expected to be true adaptive SNPs. In model species, it has become
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(a)
(b)
(c)
Fig. 2 The expected distribution of genetic divergence along a chromosome between populations of conifers characterized by low linkage disequilibrium (LD)
and distributed across an environmental gradient, using the ‘sea/islands’ metaphor proposed by Nosil et al. (2009). In this example, altitude variation (as
illustrated on right-side panels) results in a differential pressure of natural selection upon populations and genotypes leading to the occurrence of loci diverging
between populations. (a) Genetic divergence between populations located at different elevations in a single species reveals steep islands-of-divergence around
loci of intra-specific altitudinal adaptation, clearly separated by genomic regions with divergence levels well within the neutral expectations, that is, the ‘sea
level’. (b) In populations from hybridizing species that thrive at different elevations (the green species at low altitude, red species at high altitude, and hybrids or
introgressed individuals are found at intermediate elevations), wider islands-of-divergence are expected around loci for interspecific adaptation still separated
by ‘neutral genomic regions’ because LD has not built up enough (yet) to create a ‘continent-of-divergence’. (c) In populations from two reproductively isolated
species that may partly overlap along the altitudinal gradient, a signature of speciation-by-adaptation may be expected, that is, continents-of-divergence that
include loci involved in interspecific adaptation that have hitchhiked loci involved in the reproductive isolation.
routine to integrated diverse complementary methods; for example, approaches were recently developed that integrated association
with differentiation analyses (Berg & Coop, 2014) or functional
annotations (Daub et al., 2013). Recent work in humans using a
variety of approaches has shown that selection at the phenotype
level may translate into hundreds of advantageous alleles (Turchin
et al., 2012). Given quantitative genetics predictions, many
adaptive polymorphisms are expected when testing SNPs for
adaptation in conifers; therefore, the integration of different
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methods may facilitate their detection in conifer genomics as
observed in other systems such as human (Turchin et al., 2012).
Despite the significant adaptive trait variations, the high level of
standing genetic diversity (potentially serving as material for
adaptation to selective environmental factors) and the evidence for
many genetic polymorphisms involved in adaptation, it clearly
appears that conifers did not experience the level of evolutionary
radiation driven by adaptation that is reported for angiosperms
(Leitch & Leitch, 2012). The low levels of LD and the
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independence between genes and sites within genes are likely to
have differential consequences upon the evolution of conifers in the
context of speciation-by-adaptation compared to angiosperms.
Is there speciation-by-adaptation in conifers? Within a speciation-by-adaptation framework, conifers represent an interesting
case given their unique combination of genetic and genomic
attributes. When many alleles with small effects are proposed to
underpin adaptation as is the case for conifers, low islands-ofdivergence that are hard to distinguish from the neutral sea-level are
expected (Feder et al., 2012). In such cases, reproductive isolation is
more likely to result from ‘continents-of-divergence’ arising from
several adaptive polymorphisms linked by LD than from islandsof-divergence (Fig. 2c; Yeaman, 2013). Local LD could build up
structural variations arising from nonhomologous recombinations,
for instance, and thus maintain haplotypes of favorable alleles
(Yeaman, 2013); however, the low LD characteristic of conifer
genomes would typically prevent the emergence of continents of
divergence through hitchhiking, and conifer genomes appear to be
less prone to structural variations and rearrangements than
angiosperm genomes (as discussed in section II.2. ‘Contemporary
issues and emerging challenges’).
It has also been advocated that higher LD and islands-ofdivergence might build from positive selection acting on clusters of
adaptive loci arising from new mutations and favorable genomic
rearrangements (Yeaman, 2013). In conifers, long generation time,
large genome sizes and the relative scarcity of structural rearrangements (Leitch & Leitch, 2012) also reduce the likelihood of this
scenario. A low likelihood for the appearance of continents-ofdivergence is also suggested by the frequent observation of adaptive
alleles separated by sea-level polymorphisms consistent with
particularly low LD (Achere et al., 2005; Namroud et al., 2008;
Prunier et al., 2011). The islands-of-divergence may be particularly
steep in conifers (Fig. 2a) and encompass few genes (as few as one
single gene); therefore, they would not be prone to integrate loci
leading to reproductive isolation through hitchhiking.
These arguments lead to the hypothesis that conifer genomes
may be nearly impermeable to speciation-by-adaptation which
would contribute to maintaining their slow rate of evolution. As a
whole, adaptive studies to date have inadequately surveyed whole
gene’s sequence in response to adaptation to test this hypothesis.
NGS and RNA-seq to study adaptive evolution in conifers The
contribution of natural selection in conifer evolution can be
investigated by finely describing the genome architecture of
adaptation and studying adaptive divergence in different species.
The basic approach involves estimating the extent of divergence
around adaptive polymorphisms and requires extensive sequence
and polymorphism data. In recent years, RNA-seq has become a
major technique to access gene polymorphisms without requiring a
homologous genome reference and has been applied to conifers
(Chen et al., 2012; Wang et al., 2013; Yeaman et al., 2014).
Because RNA-seq facilitates de novo characterization of polymorphisms, it reduces discovery bias related to the identification of
polymorphisms in one species subsequently scrutinized in another
species (Nosil, 2012). A potential drawback of RNA-seq is biased
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estimation of allele frequencies associated with differential expression of alleles in diploid tissue sample (De Wit et al., 2015). Because
RNA pools vary in complexity between tissues, the tissue sampling
strategy will determine both the number and the nature of
polymorphisms detected. One potential approach that is unique to
conifers (gymnosperms) for overcoming biases or limitations
involves characterizing populations by using megagametophytes, a
haploid tissue that was reported to contain c. 75% of the known
gene transcripts (Verta et al., 2013; see next section IV.2. ‘Role of
gene expression variation in conifer adaptability’).
Another requirement for studying islands-of-divergence is the
ability to locate and order polymorphisms along a physical map or a
high density genetic map of the genome (Renaut et al., 2013).
RNA-seq for polymorphisms may be ordered within a single gene;
however, ordering polymorphisms in neighboring genes represents
a challenge given the present status of even the most contiguous
genome assemblies (Birol et al., 2013; Neale et al., 2014) and the
density of the most recent genetic maps (Pelgas et al., 2011; Neves
et al., 2014). Delineating pseudo molecules with continuous
advances of genome sequencing, shotgun assembly and genome
mapping could overcome this obstacle in the near future, at least in
a few highly investigated species.
Different patterns of divergence distribution along the genome
would be expected in conifers, according to the degree of adaptive
divergence and reproductive isolation (Fig. 2). Broadly distributed
species should be studied first because they have a higher probability
of adaptive variations given the environmental variability within
their geographic ranges. The best described conifers to date have
wide ecological amplitudes; therefore, they are well-suited for
studies of adaptation comparing populations in diverse environmental conditions. At the intraspecific level, the islands-ofdivergence would be particularly steep (Fig. 2a) as discussed. In
contrast, higher and wider islands-of-divergence would be expected
in pairs of hybridizing species because the divergence time would
predate the within-species divergence (Fig. 2b). However, the lack
of reproductive isolation suggests that islands-of-divergence would
remain limited because they would not encompass polymorphisms
leading to reproductive isolation (Fig. 2c). In order to test this
hypothesis, both inter- and intraspecific levels should be investigated by sampling at least two not introgressed populations in each
hybridizing species. Finally, comparing reproductively isolated
species from the same genus with overlapping natural ranges may
permit to size the islands-of-divergence related to specific environmental parameters differentiating the species’ ecological niches
(Fig. 2c). Here again, comparing the size of islands-of-divergence at
both intra- and interspecific levels will require sampling several
populations from each species. These three levels of investigation
represent a continuum of speciation that would be particularly
informative to measure the impact of adaptation in conifer
evolution.
2. Role of gene expression variation in conifer adaptability
Physiological and comparative gene expression profiling Gene
expression in conifers has been most extensively investigated in
physiological changes linked to phenotypic plasticity in diverse
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traits and responses to changing conditions (see Table 1). The
premise for this research is that transcriptome reorganisation
underpins tissue differentiation (Table 1a), developmental changes
(Table 1b), and responses to both biotic (Table 1c) and abiotic
(Table 1d) stress factors. Microarray and more recently, RNA-seq,
transcript profiling have identified differentially expressed genes
belonging to cellular and biochemical pathways; results have been
recently reviewed for topics such as wood formation (Carvalho
et al., 2013) and defense (Kolosova & Bohlmann, 2012). The
number of differentially expressed genes varies widely from just a
few to several thousand, representing < 1% to > 25% of the genes
tested, depending on the experiment and statistical criteria
(Table 1). The number of differentially expressed genes is largest
when comparing tissue types (Yeaman et al., 2014; Raherison et al.,
2015) (Table 1a), as seen in other systems as well (Brawand et al.,
2011). A transcriptome-wide understanding of the changes is being
developed by reconstructing gene expression networks (Lorenz
et al., 2011; Raherison et al., 2015), and underlying functional
relationships are being explored with promoter–transcription
factor interactions (Duval et al., 2014). Gene expression has also
identified numerous candidate genes for association mapping
aimed at delineating the genetic architecture of quantitative trait
variations (Brown et al., 2004; Holliday et al., 2010).
Comparative gene expression profiling has developed as a
complementary strand of investigation which contrasts individuals
with different phenotypes (under same conditions) or genotypes
(see Table 2). Recently, this approach has been effective for
discovering key genes in defensive pathways by exploiting
intraspecific variation in resistance to insect herbivores (Verne
et al., 2011; Mageroy et al., 2015) or fungal pathogens (Liu et al.,
2013) (Table 2a). Researchers have also targeted genotypic variations in wood properties (Li et al., 2011; Liu et al., 2013) and
populations adapted to different climates (Holliday et al., 2008)
(Table 2a). Interspecific contrasts are also emerging and provide
insights into the conservation of gene expression variation between
species, which was found to be very strong for tissue preferential
expression (Raherison et al., 2012) and strong but variable for
responses to heat and water stresses (Yeaman et al., 2014)
(Table 2b). Understanding how expression variation emerges and
contributes to evolution and adaptation represents a challenging
but important goal for the field of conifer genomics.
Expression evolution in conifers Divergence in gene expression
has long been hypothesized to be more important to species
evolution than divergence in gene function (King & Wilson,
1975). Despite the intense search for signatures of adaptive gene
expression divergence, major open questions remain and conifers
could be uniquely informative for addressing some of the
knowledge gaps. These include expression divergence in the face
of strong gene flow or in outbreeding populations, which are not
well represented by traditional research model organisms. Patterns
of differential gene expression that are compatible with an adaptive
hypothesis have been observed in Sitka spruce (Picea sitchensis)
cold-hardiness (Holliday et al., 2008). Drawing conclusions from
natural expression variation observed in wild populations is far
from simple, as multiple genetic parameters will determine the
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levels of observable expression variation in a natural setting. Here,
we discuss how the population genetics landscape typical to conifers
is likely to shape expression variation, and the genetic dissection of
expression variation to understand adaptive divergence in gene
expression.
We may predict that the high levels of genetic variation typical of
conifers are associated with ample expression variation. Because
stabilizing selection seems to be the dominant evolutionary force
acting on gene expression, most of the expression variation is
expected to be deleterious and selected against (Jordan et al., 2005;
Fay & Wittkopp, 2008; Bedford & Hartl, 2009; Brawand et al.,
2011); however, some component is likely to be selectively neutral
and scale with sequence divergence within the constraints posed by
minimal and maximal expression levels (Whitehead & Crawford,
2006).
Our current knowledge of the evolutionary importance of
expression variation has largely been derived from studies of a few
strains or genotypes of well-established model organisms. Therefore, the balance between beneficial, neutral, nearly neutral and
deleterious expression variation is not understood in natural
populations exposed to the whole spectrum of selection forces,
although results are emerging in species such as stickleback fish
(Leder et al., 2014). In this context, the numerous germplasm
collections and common-garden experiments developed in conifer
breeding (Neale & Kremer, 2011) provide a valuable resource for
dissecting expression variation into genetic and environmental
components and pinpoint expression traits that show signatures
compatible with selection, such as seen in population differentiation.
The dynamics of expression variation within a population are
also intimately connected with dominance relationships between
alleles, which influence both the fixation of beneficial mutations
and the persistence of deleterious recessive variations under a
mutation–selection balance (Hartl & Clark, 2007). Because large
populations provide a large mutational target and facilitate
efficient natural selection, gene expression in conifers with wide
ranges and large ecological amplitudes is expected to evolve under a
dynamic mutation–selection balance. On the one hand, as even
low levels of recessivity increase the frequency of deleterious alleles
in a population (Hartl & Clark, 2007), such variation may persist
and represent a significant genetic load (Lemos et al., 2008;
Charlesworth & Willis, 2009) as reported in conifer populations
(Karkkainen et al., 1996). The dynamics of recessive expression
variation are likely to differ from that expected in self-fertilizing
and largely homozygous plants, where phenotypic masking by
dominance relationships is more restricted and purging of
detrimental expression variation is more efficient (Zhang et al.,
2011). On the other hand, even a moderately beneficial expression
divergence can be under efficient selection in conifer populations
because of their large effective size which lessens the effect of
genetic drift. However, other population level evolutionary
processes might interfere with the expected fixation outcome,
such as variable selection pressures on alleles spreading outside
their adaptive ranges (Holliday et al., 2011) or different genetic
basis for the same adaptation in different lineages (Prunier et al.,
2012).
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Comparison of seven vegetative tissue types
from aerial and below ground organs
Foliage vs root plus stem tissues
Foliage vs root plus stem tissues
Oligo MA
RNA-seq
Picea glauca
Pinus contorta
P. glauca 9 P. engelmannii
Cambial tissues at the active vs dormant stages
Cambial tissues at the active vs reactivating stages
Cambial tissues at the reactivating vs dormant stages
Shoot tips during bud formation after transfer to short days
(t = 1, 3, 7, 14, 28, 70 d after transfer)
Needles at late summer (transition stage) vs early winter (dormancy stage)
RNA-seq
cDNA MA
cDNA MA
cDNA MA
cDNA MA
Cunninghamia lanceolata
Picea glauca
Picea sitchensis
Pinus taeda
Pinus pinaster
Bark of control vs white pine weevil (P. strobe) treated trees
Needles of resistant trees; un-infected vs infected with white
pine blister rust (Cronartium ribicola)
Needles of susceptible trees: un-infected vs infected with C. ribicola
Needles, control vs methyl jasmonate treated trees
cDNA MA
RNA-seq
Pinus monticola
Needles of control vs jasmonic acid treated trees
RNA-seq
Larix gmelinii
Picea sitchensis
Sample types
Methods1
Species
(c) Defenses and responses to biotic factors
Early (wood formation) vs latewood (cessation of growth and dormancy)
Oligo MA
Cryptomeria japonica
Early vs latewood of low specific gravity
Early vs latewood of high specific gravity
Xylem at five time points within a growing season
Sample types
Methods1
87 (4)
110 (5)
667 (19)
2224 (10.2)
4415 (7.3)
883 (1.5)
4018 (6.7)
4660 (43)
10 380 (57)
Significant
23 000
9720
51 157
Tested
562 (2.4)
2767 (5.4)
789 (3.4)
2382 (24.5)
Yang & Loopstra (2005)
Paiva et al. (2008)
adjP ≤ 0.0001
Men et al. (2013)
adjP ≤ 0.001;
|ratio (log2)| ≥ 1
adjP ≤ 0.05;
|ratio (log2)| ≥ 0.6
adjP ≤ 0.05;
|ratio (log2)| ≥ 0.6
Liu et al. (2013)
Ralph et al. (2006)
References
Statistical criteria3
Holliday et al. (2008)
El Kayal et al. (2011)
adjP ≤ 0.01;
|ratio| ≥ 1.5
adjP ≤ 0.05;
|ratio (log2)| ≥ 2
adjP ≤ 0.01
Wang et al. (2013)
Mishima et al. (2014)
P ≤ 0.05, 0.2;
|ratio (log2)| ≥ 1
adjP ≤ 0.001;
|ratio (log2)| ≥ 2
Yeaman et al. (2014)
adjP ≤ 0.01
References
Raherison et al. (2015)
adjP ≤ 0.05
Statistical criteria3
References
Statistical criteria3
2383 (4.7)
Significant
No. of features or
sequences2
3512
2171
21 840
10 400
59 669
18 082
Tested
No. of features or
sequences2
8131 (34)
6695 (28.5)
18 052 (76)
23 853
23 889
23 519
Significant
Tested
No. of features or sequences2
Species
(b) Comparative analyses of developmental stages
Sample types
Methods1
Species
(a) Comparative analyses of tissue types
Table 1 Gene expression profiling of development and stress responses in conifer trees. Selected studies represent the diversity of species and biological questions investigated in conifers, as well as the
different methods (microarrays, RNA-Seq) and the numbers of genes tested
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Pinus oligo MA
Pinus radiata
Compression vs normal wood
Embryonic callus generated at cold (18 °C) vs warm
(30 °C) temperature
Needles of trees grown under seven treatments varying in
temperature, humidity and day length.
RNA-seq
RNA-seq
RNA-seq
cDNA MA
Chamaecyparis obtusa
Picea abies
Picea glauca 9 P. engelmannii
Pinus contorta
Pinus taeda
Hypocotyls were grown under continuous red vs far-red light
P.taeda cDNA MA
Pinus sylvestris
12 523
644 (5.1)
496 (7.2)
11 658 (48.8)
2445 (9.2)
23 889
26,496
6841
6413 (27.3)
2875 (7.1)
1608 (1.1)
Significant
23 519
40 602
143 723
Tested
1761 (1)
23 084 (13)
12 718 (7.2)
FDR = 1;
|ratio| ≥ 1.5
adjP ≤ 0.001;
|ratio (log2)| ≥ 1.5
adjP ≤ 0.05;
|ratio (log2)| ≥ 0.95
Ranade et al. (2013)
Villalobos et al. (2012)
Lorenz et al. (2011)
Yeaman et al. (2014)
Sato et al. (2014)
Yakovlev et al. (2014)
adjP ≤ 0.05
|ratio (log2)| ≥ 1
adjP ≤ 0.01
References
Dubouzet et al. (2014)
Adomas et al. (2008)
adjP ≤ 0.01;
|ratio (log2)| ≥ 0.3
adjP ≤ 0.01;
|ratio (log2)| ≥ 1
References
Statistical criteria3
Statistical criteria3
294 (13.9)
16 (0.8)
10 (0.5)
Significant
2
cDNA MA, cDNA microarray; oligo MA, oligonucleotide microarray; RNA-seq, RNA sequencing;
No. of features or sequences, represent genes, transcripts, contigs or probes; (%), The number in parentheses is the proportion of significant features or sequences relative to the total number tested;
3
Statistical significance criteria: P, P-value; adjP, adjusted P-value.
1
Compression vs normal wood
cDNA MA
Pinus pinaster
Well-watered vs drought-stressed roots in young trees
Sample types
Methods1
Species
175 614
2109
Tested
No. of features or
sequences2
No. of features or
sequences2
Roots, control vs saprotrophic fungus (Trichoderma aureoviride) inoculated,
15 days post inoculation.
Roots of control vs mutualistic fungus (Laccaria bicolor) inoculated trees,
15 days post inoculation.
Roots, control vs pathogenic fungus inoculated (Heterobasidion annosum),
15 d. post inoculation
Mucilaginous xylem of control vs ethephon treated trees, 8 weeks post treatment
Xylem (woody fibrous tissue) of control vs ethephon treated trees,
8 weeks post treatment
Bark of control vs ethephon, 8 weeks post treatment
P. taeda cDNA MA
Pinus sylvestris
(d) Responses to abiotic stress factors
Sample types
Methods1
Species
(c) Defenses and responses to biotic factors
Table 1 (Continued)
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cDNA MA
RNA-seq
cDNA MA
cDNA MA
Picea sitchensis
Pinus monticola
Pinus taeda
Pinus radiata
Secondary xylem and phloem of the main stem of young trees.
Hybridization results were compared to identify genes
that vary similarly among spruce species
Needles of trees grown under seven treatments varying
in temperature, humidity and day length. Sequences
and expression results from the two species were compared.
Oligo MA
RNA-seq
Picea glauca, P. mariana, P. sitchensis
Picea glauca 9 P. engelmannii, Pinus contorta
562 (2.4)
51 (2.3)
131 (6)
112 (3.4)
295 (0.9)
789 (3.4)
2224 (10.2)
486 (2.1)
191 (1)
Significant
5794
orthologs
23 853
Tested
4298 (74)
Vary similarly
5407
Vary similarly
Significant
No. of features or
sequences2
3320
2171
23 000
21 840
23 853
17 825
Tested
No. of features or
sequences2
Yeaman et al. (2014)
Raherison et al. (2012)
adjP ≤ 0.01
adjP ≤ 0.01
References
Li et al. (2011)
Yang & Loopstra (2005)
Liu et al. (2013)
Holliday et al. (2008)
Mageroy et al. (2015)
Verne et al. (2011)
References
Statistical criteria3
P ≤ 0.05
adjP ≤ 0.01
adjP ≤ 0.05;
|ratio (log2)| ≥ 2
adjP ≤ 0.05;
|ratio (log2)| ≥ 0.6
adjP ≤ 0.05;
|ratio (log2)| ≥ 0.6
adjP ≤ 0.05
Statistical criteria3
2
cDNA MA, cDNA microarray; oligo MA, oligonucleotide microarray; RNA-seq, RNA sequencing.
No. of features or sequences, represent genes, transcripts, contigs or probes; (%), The number in parentheses is the proportion of differential of features or sequences relative to the total number of tested;
3
Statistical significance criteria: P, P-value; adjP, adjusted P-value.
1
Comparisons
Methods1
Species
(b) Interspecfic comparisons
Needles of susceptible vs resistant trees to the spruce
budworm (Choristoneura occidentalis)
Needles comparing populations adapted to different climates
Oligo MA
Picea glauca
Needles of resistant trees; un-infected vs infected with
white pine blister rust (Cronartium ribicola)
Needles of susceptible trees: un-infected vs infected with C. ribicola
Earlywood of low vs high specific gravity
Latewood of low vs high specific gravity
Earlywood of high vs low stiffness
Latewood of high vs low stiffness
Bark of susceptible vs resistant trees to the white pine weevil (Pissodes strobi)
cDNA MA
Picea glauca 9 P. engelmanni
Comparisons
Methods1
Species
(a) Intraspecific comparisons
Table 2 Comparative gene expression profiling of phenotypically or genetically differentiated trees
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There are also several conifer species that have small or
fragmented populations with more restricted gene flow (Alberto
et al., 2013) although they have generally been less studied. In small
populations where the strength of selection relative to drift is
expected to be reduced, a larger proportion of expression variation
is likely to occur mainly driven by neutral processes (Khaitovich
et al., 2006; Rockman et al., 2010). Small or fragmented conifer
populations are also susceptible to buildup of genetic load due to
less efficient purging of slightly deleterious expression variation.
Approaches for studying expression evolution in conifers The
level of heritable expression variation in natural populations
depends on many aspects of species’ biology, such as population
characteristics and evolutionary history. As the effects of these
parameters are especially challenging to discern in any population
or species comparison, developing an understanding of the genetic
basis of gene expression variation likely represents the best starting
point to gain insights into expression evolution in conifers.
Expression divergence between populations can be due to the
effects of a few loci with manifold phenotypic effects or multiple
loci with incremental influence on expression levels (see Fig. 3,
hypotheses). In the first case, changes in allele frequencies can have
large effects on expression phenotypes irrespective of whether the
changes result from selection or drift. In the latter case, selection
must act on multiple loci strongly enough to overcome the effects of
drift for the phenotype to have an adaptive value. In order to dissect
the evolutionary importance of expression variation, we need to be
able to determine between these types of scenarios.
The biological characteristics of conifers such as late sexual
maturity and obligate outbreeding rule out many experimental
approaches used to investigate the genetic basis of expression
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variation in model systems (e.g. pure lines or recombinant inbreds)
because they are impractical or would take decades to implement.
However, the conserved seed morphology of gymnosperms and
analysis of megagametophytes may provide a useful alternative for
developing an approach that does not require any breeding (Fig. 3,
system). Like all land plants, gymnosperms have a haplodiplontic
life cycle where gamete (reproductive cell) formation involves a
vegetative haploid state between meiosis and gamete formation
(reviewed in Williams, 2009). The gametophyte development in
conifers starts with meiosis leading to the production of pollen in
the male strobili and a megagametophyte in the female strobili. The
female megagametophyte grows to c. 2500 cells through mitosis
and produces archegonia, cell structures that are fertilized by the
male reproductive cell. A mature seed contains a fully developed
diploid embryo surrounded by the haploid megagametophyte
(Fig. 3, seed development).
Forest genetics research has used the meiotic megagametophyte
extensively to identify allelic protein variants (isozymes) or for
genetic mapping purposes. For example, megagametophyte segregation analysis identified a null mutant of the cad gene P. taeda and
led to the discovery of unexpected metabolic plasticity in lignin
biosynthesis (MacKay et al., 1997). Genetic mapping, most
recently by Neves et al. (2014), and genome sequencing (Nystedt
et al., 2013; Neale et al., 2014) have capitalized on the haploid
nature of the megagametophytes to reduce genotypic complexity.
The megagametophytes can also be used to study the genetic basis
of gene expression variation in natural conifer populations by
analyzing series of megagametophytes from a single tree (Verta
et al., 2013). Because each megagametophyte is a haploid product
of meiosis, heterozygous loci in the mother tree segregate 1 : 1 in the
haploid megagametophytes (Fig. 3, system). Sequencing-based
Fig.
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approaches (RNA-seq) are especially attractive to discern between
alleles with expression differences because they facilitate parallel
identification of genotypes and gene expression levels from the
same data set (Fig. 3, RNA-sequencing), and they identify
expression quantitative trait loci (eQTL).
The megagametophyte approach could contribute a missing link
between diversity and function in the burgeoning set of genomic
resources available for keystone conifer species (Rigault et al., 2011;
Raherison et al., 2012; Nystedt et al., 2013; Pavy et al., 2013; Neale
et al., 2014). Because of its experimental simplicity, it can be
applied to any wild individual or gymnosperm species with large
enough seeds and thus facilitate population-wide analyses. Initial
steps towards the application of megagametophyte analysis to
natural conifer populations have already taken place (Verta et al.,
2013). Allelic expression variation was found to be abundant and
largely unique to individuals when comparing two trees of the same
white spruce population. An overall low level of pleiotropy was
found and suggested that selection affecting the expression in one
gene may have little consequence on the expression of other genes
(Verta et al., 2013). Expression variation was over-represented
in stress-response genes, indicating its potential significance in
adaptation to biotic and abiotic stress factors, as also proposed
in humans (Ye et al., 2013). A logical next step is to take this
approach to the population level, with the promise of uncovering
the genetic architecture of gene expression traits involved in
adaptation and evolution (Fig. 3, hypotheses). The role and genetic
structure of expression divergence could be investigated by
comparing locally adapted populations (Mimura & Aitken,
2010) or central and marginal populations (Savolainen et al.,
2007) and by analysing the development and maintenance of
reproductive barriers (De La Torre et al., 2014), among other areas.
V. Emerging research directions toward a more
integrated understanding of genetic diversity and
genome function
Research outputs from the human genome have provided an
understanding of the functional consequences of many different
types of genetic or genomic variations associated with heritable
disorders, predisposition and development of cancer, and changes
associated with development and aging, among other areas of
inquiry. Structural variations, gene copy number variations
(CNVs), epigenetic changes such as DNA methylation, and
regulatory controls by noncoding RNAs and other functional DNA
elements represent major mechanisms of genetic variation from
which phenotypic variation may arise. By comparison, conifer
genomics research has focused primarily on SNP variation in or
around relatively small sets of genes (Brown et al., 2004; Eckert
et al., 2009a) and has developed limited insights into the functional
basis of complex traits. We propose that analyses of diversity
encompassing more broadly the different types of impacts on
genome function are needed to address emerging challenges that
face conifer trees and forests. For example, understanding adaptability to changing conditions depends on phenotypic plasticity,
standing genetic variation and associated phenotypic variability,
and the interactions between them.
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Direct or indirect effects on gene expression are common
denominators of several types of genomic variation which have
been under-studied in conifers to date, including epigenetic
control, CNVs (through dosage), sequence variations in regulatory
elements (noncoding DNA) and variations at trans-acting loci.
Several authors (Jordan et al., 2005; Wray, 2007) have argued from
first principles that mutations altering the level of gene expression
make a qualitatively distinct contribution to phenotypic evolution
by affecting certain kinds of traits and being acted upon more
efficiently by selection. The exact nature of that contribution is
being delineated by recent empirical evidence in systems such as
stickleback fish and Arabidopsis linking adaptation to regulatory
sequence variations and variations in gene expression responsiveness to stress, respectively (Jones & Wang, 2012; Lasky et al.,
2014). We may therefore predict that investigating regulatory and
gene expression variations side-by-side with DNA sequence
variations will lead to a more integrated understanding of the
genomic basis of adaptability. Here, we discuss two areas of
relevance that have begun to develop in conifers, that is, epigenetics
and copy-number variation.
1. Epigenetic variation
Epigenetic variation provides a mechanism for phenotypic diversity
in the absence of genetic mutation and thus plays a role phenotypic
plasticity and adaptation in plants (Schmitz et al., 2011). It was
proposed to be especially important for long-lived organisms such
as for trees (see reviews: Yakovlev et al., 2011; Br€autigam et al.,
2013). A temperature-dependent epigenetic ‘memory’ conditioned by the temperature during early embryo development was
reported in P. abies and shown to influence the timing of bud
phenology in offspring (Johnsen et al., 2005; Yakovlev et al., 2010).
Epigenetic variation affects gene expression and has been associated
with changes in DNA methylation and regulation by noncoding
RNAs. In Arabidopsis, trans-generational epigenetic variation
resulting in phenotypic diversity has been directly linked to DNA
methylation altering transcription (Schmitz et al., 2011). In poplar,
DNA methylation associated with aging shaped drought responses
(Raj et al., 2011). Yakovlev et al. (2010) identified specific
noncoding micro RNAs whose differential expression indicated a
putative role in the epigenetic regulation spruce (Picea abies).
Conifers accumulate micro RNAs that include both shared and
distinct sequences compared to angiosperms (Yakovlev et al.,
2011), but in contrast to angiosperms, they appear to produce
much lower levels of 24nt small, interfering RNAs (Dolgosheina
et al., 2008), except in reproductive tissues (Nystedt et al., 2013).
Research on epigenetic memory in P. abies has contributed
significantly to our understanding of epigenetic control in
adaptability and, although the underlying mechanisms are only
partly identified, the findings are stimulating the development of a
field of ‘ecological epigenetics’ (Br€autigam et al., 2013).
2. Copy-number variation
Structural polymorphisms such as gene copy-number variations
(CNVs) epitomize the dynamic nature of genomes (Chain et al.,
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2014). Genome-wide analyses have associated CNVs with several
disease phenotypes in humans (Craddock et al., 2010), and were
associated with local adaptation in stickleback fish populations
(Chain et al., 2014). Many CNVs modify transcript levels (Schlattl
et al., 2011) and result in protein dosage effects which may be acted
upon by selection if they alter fitness. In P. sitchensis, Hall et al.
(2011) showed that weevil resistance was associated with CNVs in
enzymes involved in (+)-3-carene biosynthesis. A first large-scale
analyses in conifers was based on sequence capture analyses of 7434
genes and identified 408 putative cases of presence absence
variation (PAV) in P. taeda (Neves et al., 2014). Including finescale CNV information was suggested as a means of increasing the
resolution and power of association studies aimed at delineating the
genetic architecture of complex traits (Schlattl et al., 2011). To this
end, complete genome hybridization arrays (aCGH) have been
developed in Picea glauca and used to identify CNVs in several
hundreds of genes; much variation in affected genes was observed
between full-sib families from the same population (J. Prunier
et al., unpublished).
VI. Conclusion
Conifer genomics has made huge strides in recent years owing to
NGS as well as developments in quantitative and population
genetics, bioinformatics, and evolutionary biology. We have
argued that more integrated analyses of conifer genetic diversity
and genome function will significantly enhance our understanding
of the molecular the basis of phenotypic plasticity and adaptation as
determinants of adaptability in conifers. NGS technologies can be
deployed to reveal variations in gene expression, DNA methylation, regulatory microRNAs, CNVs and other structural variations
in addition to coding and regulatory sequence variation –
simultaneously. These methodologies may also be invaluable for
analyzing more species and thus acquiring an understanding that
spans across the diverse conifers that make up our forests.
Developments may also be expected in areas such as interacting
genomes and metagenomics, protein and gene expression networks, among others, which we have not discussed here. Genomic
selection methods (Grattapaglia & Resende, 2011) – recently
applied to hardwood and conifer trees and shown for its potential
to significantly accelerate breeding (Beaulieu et al., 2004; Resende
et al., 2012) – will also profit from higher throughput genomic
technologies. Some of the challenges that lie ahead include higher
throughput and lower cost phenotypic analyses for conifer trees to
facilitate genotype–phenotype investigations, and the development and validation of appropriate experimental systems for
analyzing conifer gene functions. Transient assays using conifer
cells were recently shown to enable hundreds of functional tests
(Duval et al., 2014) offering a homologous system which may be
advantageous to complement heterologous analyses in model
systems.
Acknowledgements
The authors thank Sebastien Caron and Isabelle Giguere for
preparing Fig. 1, Armand Seguin for the phylogenetic tree in Fig. 1,
New Phytologist (2016) 209: 44–62
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Elie Raherison for assistance with the preparation of Table 1. J-P.V.
and J.P. were supported by grants from the Natural Sciences and
Engineering Research Council of Canada, the Quebec Ministry of
the Economy, Innovation and Exports, and Genome Canada,
Genome Quebec and Genome British Columbia for the
SmarTForests project (J.J.M.). This review is an output of the
partnership between the SMarTForest project and the ProCoGen
European Network.
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