Genomic approaches for

dissecting fitness traits in forest

tree landscapesCiro DE PACE

University of Tuscia, Viterbo, Italy

• The human footprint on natural ecosystems is now almost inescapable.

• Pressures of habitat loss, pollution, overexploitation, invasive species and climate change are increasing in reach and intensity around the globe.

• Their cumulative effects are eroding biodiversity and altering ecological processes, triggering concerns that we are approaching a planetary-scale tipping point.

• When the mentioned changes occur in forestsforests, the expected consequences are severe for biodiversity and for the local people benefiting of forest services.

• The inability to move (‘sessility’) and the slow dispersal ability through seeds, impede forest trees, in the short term, migration to the habitats that maximize their fitness.

• One consequence is that ecological and genetic traps ecological and genetic traps rise when forest trees remain in the habitat where their fitness is lower than in other available options.

Ecological Ecological traptrap

19751975 20092009

The purposes of this presentation are:

• (1) describe the assumptions needed to measure differential fitness in a forest tree population to predict the genetic changes due to Natural Selection and avoid ecological and genetic trapecological and genetic trap,

• (2) use a literature example to display the empirical evidence for the existence of differential fitness,

• (3) develop a conceptual framework, based on genomics, within which the fitness of forest trees can be better understood,

• (4) propose some perspectives on genomic approaches to improve fitness measurements in heterogeneous environments

The main assumptions needed to measure differential fitness are:

(1a) Differential fitness depends on Differential reproduction

(1b) Aspects of the life-history of an individual genotype affect its reproductive efficiency

(1) Genetic changes in a forest tree population due Natural selection

require differential fitness among genotypes.

Differential reproduction is measured by the ‘reproductive efficiency’ which provide a direct estimate of the genotype fitness. The fitness is often expressed as a relative, not absolute, measure of reproductive efficiency. One component of the relative fitness is plantplant fertilityfertility

1a) Differential fitness depends on Differential reproduction

High fertility

Low fertility

The number of female inflorescences provide another proxy estimate of plant fertility

Seedling viability, rate of development, mating success, etc., are life history traits affecting fitness and are known as survivalsurvival fitness components.fitness components. Seedling viability Seedling viability is the main life-history trait affecting differential fitness.

Seedlings viability over time informs us about the strengths of post-fertilization barriers and the local adaptation of the different seedling genotypes.

(1b) Aspects of the life of an individual genotype that affect its reproductive efficiency

The general assumption for the occurrence of

differential fitness among individuals of the

population, , is that::There must be Genetic DiversityGenetic Diversity for the plant trait putatively affecting fertility and survival in the forest tree population.

Estimate the individual net fitness in a (Mendelian) population when genetic diversity of the fitness-related trait occur at one locus with two alleles

Individual genotype AA Aa aa

Number of seeds produced (fertility) 100 150 80Fertility fitness 100/150=0.67 150/150=1 80/150=0.53

Number of seeds germinated 89 132 75Number of seedlings survived 70 120 50Survival fitness 70/120=0.58 120/120=1 50/120=0.42

0.67 x 0.58=0.39 1 x 1 = 1 0.53 x 0.42=0.22

Net fitness w1 = 0.39 w2 = 1 w3 = 0.22

Mendelian population

Prediction on the effect of Natural selection in a Mendelian population (1 locus, two alleles) with differential fitness among genotypes

DIRECTIONAL SELECTION AA

(w1) Aa

(w2) aa

(w3)Selection favouring the individuals with the dominant allele 1 1 < 1Selection favouring the individuals expressing the recessive allele < 1 < 1 1Selection favouring the individuals expressing the heterozygous genotype < 1 1 < 1Selection against the individuals expressing the heterozygous genotype 1 < 1 1

Differential fitness among genotypes of the Mendelian population

(2) Empirical evidence for the existence of

differential fitness in Mendelian populations

(2a) Resistance of sugar pine (Pinus lambertiana) to blister rust caused by the Cronartium ribicola

fungal pathogen

(2a) The resistance of sugar pine (Pinus lambertiana) to blister rust (Cronartium ribicola) display Mendelian inheritance

Kinloch Jr, B. B., Parks, G. K., & Fowler, C. W. (1970). White pine blister rust: simply inherited resistance in sugar pine. American Association for the Advancement of Science. Science, 167(3915), 193-5.

Progenies of sugar pine completely susceptible (right) and segregating (left)for resistance to white pine blister rust.

Blister rust canker showing heavy resin flow. Canker bark has been eaten by rodents.

Early symptoms

Late symptoms

Spindle-shaped blister rust canker on branch of a young western white pine. (Courtesy O. Maloy)

(2b) Because of scarcity of evidences for genetic diversity for morphological traits related to fitness in forest tree populations, genetic markers were used to identify genotypes expressing differential fitness• Isozyme biochemical markers

•DNA genetic markers revealed by (a) probe hybridization and (b) PCR (Polymerase chain reaction)

IsozymesIsozymes are biochemical markers identified by electrophoretic techniques. •They are alternative forms of catalytic proteins encoded by different alleles at the same locus. •No more than 20-30 loci were possible to mark in one experiment, and with few exceptions, alleles for isozymes were found adaptive-neutral. •They helped in making inferences on demographic patterns and colonization dynamics in several conifers and Fagaceae species

FS FS FF SS FF FF FS FF FF SS FF FS FF FS FS FF SS FF FF FS FF FF SS FF FS FF SSSS

Electrophoretic pattern for Glutamate-oxalate transferase (GOT) isoenzymes

Superoxide dismutase (SOD)

Phenotype

Genotype

FS FS FS FS FS FS FS FSFS FS FS FS FS FS FS FS

Phenotype

Genotype

DNA makers revealed by hybridization to labeled nucleic acids had the same limitations of isozyme markers

VNTR (Variable Number of Tandem Repeat) (Multilocus but genotyping is difficult)

RFLP (Restriction Fragment Length Polymorphism) (Unilocus)

Multilocus DNA makers revealed by PCR were promising for preparing dense linkage maps but not to study fitness in populations

• AFLP (Amplified fragment length polymorphism)

Here is a stained PAGE gel displaying segregation for three loci (a, b, c) used for preparing a linkage map of maritime pine (Pinus pinaster Ait.) based on AFLP

Example of AFLP profile showing the three types of segregation. Lanes 1 and 2 correspond to the parents (female and male) and other lanes correspond to the full-sib progeny. (A) Inter-cross marker, heterozygous in both parents and segregating 3:1 in the progeny; (B) Test-cross marker, heterozygous in the male and absent in the female, and segregating 1:1 in the progeny; (C) Test-cross marker, heterozygous in the female and absent in the male, and segregating 1:1 in the progeny.Chagné, D., Lalanne, C., Madur, D., Kumar, S., Frigério, J. M., Krier, C., ... & Brach, J. (2002). A high density genetic map of maritime pine based on AFLPs. Annals of Forest Science, 59(5-6), 627-636.

Next Generation Sequencing

The recent development of next-generation sequencing platforms has helped to revolutionize

population genetics by providing rich databases for genetic markers that detect polymorphism at the single nucleotide of the DNA template

(Single Nucletide Polymorphism, SNP)

SNP are codominant markers spread at million loci within the nuclear genome

Morin, P. A., Luikart, G., & Wayne, R. K. (2004). SNPs in ecology, evolution and conservation. Trends in Ecology & Evolution, 19(4), 208-216.

Locus A

Locus nLocus 2Locus 1

n > 106

Several sequencing platforms are available for SNP

genotyping

Huang, C. W., Lin, Y. T., Ding, S. T., Lo, L. L., Wang, P. H., Lin, E. C., ... & Lu, Y. W. (2015). Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection. Microarrays, 4(4), 570-595.

Normalized DNA plate

Double enzyme digestion

Adapter ligation(24-plex inline barcodes on P1)

Purification

24-plex pooling

Gel-based size selection(330-480bp, considering adapters)

Simulation on Vitis vinifera genome:SphI+MboI @240-390bp

~20,000 expected loci

Amplification (Indexed primers)

de novo multiplexing sequencing of reduced representation library of a tree genome based on restriction enzymes and PCR amplification of the library of fragments, speded-up genome –wide (GW) SNP identification and genotyping.

Analysis pipeline for Analysis pipeline for de novo de novo SNP discovery/genotypingSNP discovery/genotyping

SNPs are used primarily for: • Detecting population structure and measure

genetic diversity between populations• Association studies for dissecting QTLs for fitness-

related traits• Landscape genomics studies

698 fixed SNPs, GTR model

HP1 - macrocluster

HP2 macrocluster

EVG-S and EVG-Doriginated from NOC x TGR cross

HP1 and HP2 are two hazelnut population at the edges of a naturally regenerated deciduous

forest in Nortern and North-Eastern, respectively, territory of the Latium region in Italy.

Detecting population structure and measure genetic diversity between hazelnut (Corylus avellana) populations

Population structure, K=3

HP1 HP2

TGRrosa unlabeled

Cultivated azelnuts

“Giresun”, turkish accessions

SNP genotyping are useful for fine mapping of linked loci and for association for detecting allele associations at loci affecting fitmess.

Morin, P. A., Luikart, G., & Wayne, R. K. (2004). SNPs in ecology, evolution and conservation. Trends in Ecology & Evolution, 19(4), 208-216.

Locus A Locus B

Equilibrium (null hypothesis)

Linkage disequilibrium (D=0.1)

A B

a b

0.26 ab

Locus C

A

B

C

c

C

Genetic mapping in experimental

poplations Linkage disequilibrium studies

Genome-wide association (GWA) Genome-wide association (GWA) mapping to identify SNP mapping to identify SNP

molecular markers explaining molecular markers explaining variation for fitness-related traits. variation for fitness-related traits.

The first case of GWA involving SNPs and a fitness trait, is that of “serotiny” in Rocky

Mountain lodgepole pine (Pinus contorta) cones

Throughout much of its range, lodgepole pine (Pinus contorta Dougl.) produces serotinous and non-serotinous cones. Serotinous cones do not open at maturity because of a resinous bond between the cone scales. Cones open and release seeds only n years when soil temperature reach 45-50 °C or even higher due to wild fire.

Lodgepole pine seedlings emerging in five-year-old forest fire site in central Idaho

Serotinous cones

Non-serotinous cones

51 non-serotinous plants

47 serotinous plants Locus 1 Locus 2

A A

A’ A’

Locus 97,616

SNP locus Depth

Minor allele freq.

Probability of serotiny for the SNP

genotype

AA A'A A'A'

1 5.6 0.18 0.54 0.43 0.712 3.1 0.22 0.48 0.35 0.463 5.2 0.25 0.49 0.73 0.45

….. 97616 4.3 0.21 0.57 0.27 0.24

Probability of serotiny in the sample of 98 individuals was 0.48A=allele with minor frequencyDepth represents the average number of sequences per individual obtained for each locus.

47 serotinous and 51 nonserotinous lodgepole pines plants from three populations were genotyped at 97,616 SNP loci

Loci 1 and 3 are associated to serotiny

A

A’

GWA mapping of SNP markers for serotinous and non-serotinous Rocky Mountain lodgepole pine cones

Rocky Mountain lodgepole pine forest is an example of homogeneous forest where studying trait related homogeneous forest where studying trait related to fitness is relatively easyto fitness is relatively easy.

Lodgepole forest trees occur as an even-aged, single-storied and sometimes overly dense forest.

Often, forests display an eterogeneous forest landscape composed by a tree community on a spatially variable pattern

In a complex community of forest trees, independent measure of survival and fertility is difficult to achieve separately for each species.

Forest fragmentation, mixed land cover, and patches of invasive species, reduce forest connectivity, increase the ‘island’ population structure, and new evolutionary forces come into play such as drift, assortative mating, increased inbreeding, reduced dispersal (migration) ability, etc.

The geographical distance in the ecosystem cause differential ecological processes in the forest landscape

Seed predator differentiation

Occurrence of invasive species

Differential impact of two or more stressors on the ecological response (e.g. diversity, productivity, abundance, survival, growth, reproduction) of the plant community.

LANDSCAPE ECOLOGY

• The forest landscape in heterogeneous environment may be envisioned as a mulilayered structure where the ecological landscape is one of the layer.

• Sophisticated approaches are needed to quantify spatial heterogeneity, particularly through the integration of geographic information systems (GIS) into the analysis of ecological processes in the territory.

LANDSCAPE GENETICSLandscape genetics endorses those studies that combine population genetic data, adaptive or neutral, with data on landscape composition and configuration and spatial heterogeneity.

In landscape genetics the phenomenon under consideration is the genetic structure of a population and the processes that govern it, such as gene flow or adaptive evolution

LANDSCLANDSCAPE APE GENETICGENETICSSEach individual population is represented geometrically as a node whose volume is proportional to the within-population component of genetic variance at several loci associated to fitness traits.

The intensity of interpopulation ‘genetic differentiation’ (FST) may be interpreted in terms of features of the ecological landscape.

Pop 2

Pop 3

Pop 1

Pop 1

Pop 2

Pop 3

FST

FSTFST

Geographical distance;

patchiness heterogeneity

Population geneticsPopulation genetics

Landscape ecologyLandscape ecology

FUNCTIONAL TRAITS• In more complex situation, a more productive way of

asking the classic question of what processes maintain the diversity of species, is to ask: what processes explain the what processes explain the dispersion of traits among community members.dispersion of traits among community members.

• Therefore, one way to make sense of this diversity and its mechanistic underpinnings is to focus on the functional traits they possessed by species, such as plant height, seed size or leaf area.

• It has been demonstrated that functional traits consistently pre dict the competitive interactions between trees in six forested biomes, and help in deconstructing the fitness landscape in plant communities.

S Díaz et al. Nature 1-5 (2015) doi:10.1038/nature16489

The global spectrum of plant form and function.

Chemical fingerprinting of metabolome by Chemical fingerprinting of metabolome by infrared (FTIR) spectroscopy is promising infrared (FTIR) spectroscopy is promising in identifying biomarkers of fitness in in identifying biomarkers of fitness in complex forest communities. complex forest communities. • FTIR is has been used to identify Quercus agrifolia plants

resistant to Phytophthora ramorum, the causal agent of sudden oak death, prior to infection.

• Concentrations of quercetin flavonol and ellagic acid phenolic dilactone were foud to be higly significant biomarkers of resistance.

• Therefore, chemical fingerprinting can be used to identify resistance in a natural population of forest trees prior to infection with a pathogen and speed-up discovery of candidate genes for fitness traits.

PERSPECTIVEIn the near future, deep genomics coupled to the assessment of “functional traits” and “fitness biomarkers” will promote precise genetic dissection of fitness traits in the forest tree landscape.

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Deep genomics to explore the fitness landscape.

If the cost of resistance is large, compensatory mutations will sharply increase in frequency. This prediction can be tested by determining the LD decay for polymorphic SNP in genomic regions harboring expression QTLs and structural genes associated to the resistance phenotype and its biomarkers

Fitness

a

Genotypic space for resistance Genotypic space for

compensatory mutations