Supporting Information:
Algal Culture
We obtained stock cultures of algal species from four different culture collections (Table
S1). For each species, we transferred biomass from the stock culture to triplicate 125 mL
Erlenmeyer flasks filled with 100 mL of COMBO growth medium (1). A few species
grew poorly in COMBO and instead were grown in modified Bold 3N medium (2). To
minimize contamination, we transferred algae from stock to batch cultures with a flamesterilized loop or an autoclave-sterilized pipette in a laminar flow cabinet and capped
batch cultures with a breathable 0.22 μm vented lid. Cultures were shaken continuously at
115 rpm in a 20°C environmental chamber operating on a 16:8 hour, light:dark cycle with
fluorescent lamps (4100k) providing on average 160 μE.m-2.s-1 irradiance at the shaking
platform surface. Cultures grew between 8 and 60 days, depending on the growth rate of
each species. When cultures reached a high density, we examined each replicate via
compound microscopy to confirm species identification, and the absence of visible
contaminants. Specimens and stock culture have been retained for long-term storage and
future work. We next concentrated replicate cultures into a 50 mL centrifuge tube and a
4mL sample was preserved in 1% glutaraldehyde.
Ortholog Determination
We first defined a set of primer-taxa. Primer-taxa must have fully sequenced genomes
and span the breadth of the final phylogenetic analysis. We used six algal species as
primer taxa (Chlamydomonas reinhardtii, Chlorella variabilis, Coccomyxa sp C169,
Micromonas pusilla, Ostreococcus lucimarinus, Volvox carteri). For these six species, we
estimated a phylogeny using RAxML version 7.4.8 (3) based on 18s and rbcL. We
partitioned rbcL and 18s data applying a GTR model to both partitions (based on results
from jModelTest) and used 100 bootstrap replicates to assess statistical confidence in the
topology. The resulting tree was well supported and in agreement with molecular
systematic toplogies recovered in previous studies (4). Gene families represented by one
and only one ortholog in each of the primer-taxa define our custom set of ‘core-
orthologs’. To define orthology in the primer taxa, we used EvolMAP software (5).
EvolMAP assumes a known species tree as a guide for gene clustering and subsequent
ortholog determination assuming Dollo parsimony (gene families are only lost and not
gained independently). We created two core-ortholog sets from different genomic
partitions: one set for nuclear genomes and one for chloroplast genomes. In earlier
analyses, we had also included distantly related outgroups in order to obtain the nuclear
orthologs. However, we found that adding distantly related outgroups as primer-taxa
resulted in rapid decline in the total number of nuclear core-orthologs (0 outgroup species
yielded 1846 core-orthologs; 1 outgroup species yielded 795 core-orthologs; 3 outgroup
species yielded 659 orthologs). To maximize the number of core-orthologs, and therefore
the data available for analyzing the phylogeny, we did not use an outgroup, choosing
instead to proceed with a midpoint root approach.
After determining core-ortholog sets using six algal genomes, we performed multiple
sequence alignment of each gene family using MUSCLE (6). Aligned sequences were
then used in order to build profile Hidden-Markov Models (pHMMs) using HMMbuild
from the HMMer 3.0 package (www.hmmer.org) (Figure S1). Using Blast2Go (7), we
annotated the core-orthologs using Gene ontologies where possible. We extracted all
orthologs as amino acids from the Chlamydomonas reinhardtii genome and used them as
reference sequences for the functional annotation. To identify orthologous genes in
sequence data from each species in our phylogenetic analysis, we used core-ortholog
pHMMs in the HaMStR software. After identifying a candidate ortholog ‘hit’ with a
pHMM search, HaMStR requires reciprocal highest similarity between the hit and a
reference genome. We chose the well-studied green alga Chlamydomonas reinhardtii for
the reference genome. Those hits that do not pass reciprocal highest similarity with the
reference genome are considered potential paralogs and rejected from phylogenetic
analysis. We relied on amino acid alignments for all subsequent phylogenetic analyses
due to the computational scale of the analyses, the higher stability of multiple sequence
alignments for proteins, and the strong probability of saturation of nucleotides at the
evolutionary timescale being considered (8).
Phylogenomic Methods
After removing long-branch genes that were likely artifacts, we performed a final
alignment of each gene family with MUSCLE v 3.8 (6). We refined the final alignment to
remove ambiguously aligned regions using aliscore and alicut (9). Using a perl script
(Phylocatenator tool in Osiris (10)), we constructed five different concatenated data
matrices progressively increasing the density of each matrix (Table S3). For each data
matrix, we used RAxML 7.4.8 to conduct a partitioned ML search using a WAG model
for nuclear proteins and a CPREV model for chloroplast proteins. We used unconstrained
random starting trees and evaluated topological robustness using 100 non-parametric
bootstrapped replicates (step 8). See Figure S2 for an overall workflow schematic
starting from FASTQ quality control, ESTs, and chloroplast genomes leading to the final
RAxML analysis. We estimated average bootstrap scores resulting from each different
matrix in order to determine which sampling scheme yielded the highest support values.
We used the matrix and resulting tree with the highest bootstrap support for all
subsequent analyses.
Molecular Systematics of Freshwater North American Green Algae
Green algae are extremely diverse and important primary producers, especially in
freshwater communities. They can be efficiently and rapidly grown under laboratory
conditions and many species can be ordered from culture collections. Green algae
(Viridiplantae) contain two lineages, the Chlorophyta and the Charophyta (4), with a total
estimated 22,000 species excluding land plants (11, 12). Many of these species are widely
distributed and account for a significant proportion of primary productivity in freshwater
ecosystems, some of the most threatened and critically important ecosystems in the
world. Although most major taxonomic groups of green algae are represented in
freshwater communities, members of the Chlorophyceae (Chlorophyta) and
Zygnematophyceae (Charophyta/Streptophyta) are particularly diverse in freshwater
habitats. Due to the fact that many algal species are unicellular and have short generation
times, they can be grown in small spaces under laboratory conditions, and they can be
stored in culture collections for distribution to laboratories around the world. Despite
these attributes as a potential model system bridging evolution and ecology, estimating
the oldest phylogenetic relationships among the green algae has been difficult. Support
for relationships among most major groups in studies with few genes is variable and
generally moderate (13, 14), while the use of coding and ribosomal sequences from
whole chloroplast and mitochondrial genomes has given high support for a variety of
otherwise conflicting evolutionary hypotheses (15-17). The fluidity of green algal
taxonomy and phylogeny is an obstacle to their use in phylogenetic ecological studies,
and there have been few concerted efforts to determine the relationships of common
laboratory species that could be easily used in such efforts.
Phylogenetic Relationships
Phylogenetic relationships among the sampled green algae were investigated. Among the
Charophyta, the Desmidiales and the Zygnematophyceae were found to be monophyletic
while Zygnematales was paraphyletic with respect to the Desmidiales. The genus
Staurastrum (Desmidiales, Zygnematophyceae) was not monophyletic (Figure 3). Two
strains of prasinophytes – Micromonas pusilla and Ostreococcus lucimarinus
(Mamiellales, Mamiellophyceae) – formed a monophyletic clade nested between the
Charophyta and the remaining Chlorophyta. Although outgroup taxa were not included,
the root of this tree probably lies along the branch between the Mamiellophyceae and
Charophyta.
Within the Chlorophyta, clades corresponding roughly to Chlorophyceae and the orders
Sphaeropleales and Chlamydomonadales were found to be monophyletic. Botryococcus
braunii (Trebouxiophyceae) was found to be sister to Chlorophyceae with moderate
support (Figure 3). Excluding Botryococcus braunii, the remaining Trebouxiophyceae
(all Chlorellales) were monophyletic. We found several examples where the placement of
individual taxa in this study differed from the placement of these same taxa or congeners
in other studies. Crucigenia tetrapedia was found sister to a clade of Coelastrum and
Hariotina (Sphaeropleales) while Bock et al. (18) found Crucigenia tetrapedia to be
embedded in the Trebouxiophyceae. Two taxa of uncertain affinity, Botryosphaerella
sudetica and Sphaerocystis schroeteri, were placed in the Sphaeropleales (Figure 3). The
genus Monoraphidium (Chlorophyceae) was not monophyletic.
Before using our phylogeny to guide experimental manipulation of phylogenetic diversity
in ecological studies, we discuss the reliability of our tree. Although bootstrap support
can be inflated for large genomic datasets (19), the overall high bootstrap support of our
tree bodes well for reliability. Perhaps more importantly, the congruence of our tree with
major taxonomic clades is encouraging. Nevertheless, a few unexpected results deserve
discussion.
The placement of certain taxa within our tree remains in question, due to their current
taxonomic classification. Those taxa have been labeled as incertae sedis (Figure 1). We
recovered Crucigenia tetrapedia SAG 218/3 in the family Scenedesmaceae, while recent
molecular systematic work classified it as Trebouxiophycae incertae sedis (18).
However, previous studies have not analyzed the particular strain included in our
analysis. Our results support the placement of Sphaerocystis schroeteri SAG 16.84 within
the Sphaeropleales, while AlgaeBase places it within Chlamydomonadales, which is a
separate order within the Chlorophyceae. Furthermore, our results support the placement
of Tetrastrum heteracanthum UTEX 2445 within the Trebouxiophyceae, which is
consistent with recent work (18). Many of these discrepancies may be due to
morphological simplicity of the taxa in question and various convergent characteristics
leading to difficulties in identification (18, 20).
Despite these unexpected results, our phylogeny is in the most part consistent with the
current molecular systematic literature. Algal systematics is in a state of constant flux,
requiring frequent reassessment of all levels of classification in light of new data (18, 21).
This is partly due to the immense diversity of freshwater green algae, but also due to
convergent features amongst microscopic cells that can be notoriously difficult to identify
using traditional morphological approaches. The incorrect morphological identification of
strains currently maintained in culture collections remains a significant problem for algal
systematics. A molecular phylogenetic study like ours may place a given taxon in a
seemingly incorrect position in relation to its congeners, yet that may simply be the result
of a misidentification of the taxon in culture. As algae can be difficult to distinguish
under a microscope, researchers that are planning to use them in laboratory-based
experiments should select species with resolved relationships and available genetic data
specific to the strain of interest. Furthermore, it is likely that some cultures are prone to
fungal and bacterial contamination that can significantly affect the results of competition
experiments and contaminate genetic samples with undesirable DNA. We urge caution in
the selection of experimental species (particularly those species whose taxonomic
position is still being debated), yet with these issues in mind, green algae have proven to
be reliable and highly versatile model experimental species.
Phylogenomics in Galaxy
Traditionally, the complexity of phylogenetic methodology and the lack of transparent
data availability have created barriers for inter-disciplinary research, often discouraging
researchers from incorporating phylogenetic metrics in ecological, environmental and
conservation oriented studies. Phylogenetic analyses require numerous separate steps,
each requiring different software packages and input file formats. We developed
phylogenetics tools within the Galaxy workflow management system
(http://www.galaxyproject.org) because it is an open-source, lightweight, web-based
system that can incorporate most existing bioinformatics tools (22, 23). Galaxy is flexible
enough to incorporate all steps of a complex phylogenetic analysis, while greatly
simplifying the transfer of data from one application to the next. Furthermore, tools and
scripts commonly used in R and other platforms for downstream ecological analyses can
easily be incorporated into Galaxy (24), thereby allowing researchers to perform any
manner of analysis within the system, including rapid generation of data sets for
exploration of issues like sampling density and how it affects phylogenetic trees. To
facilitate the search for orthologous genes in similar studies, we created wrappers for
EvolMAP (5), an algorithm that uses a species tree-based clustering method that joins allto-all symmetrical similarity comparisons of multiple gene sets, and HaMStR (25), a
Hidden Markov Model (HMM) based search tool to screen EST sequence data for the
presence of putative orthologs in a predefined set of genes. EvolMAP allows users to
easily extract sets of orthologous genes (core orthologs) for any taxonomic group for
which whole genomes are available. Orthologous genes are then aligned, and used to
build HMMs. Subsequently, HaMStR enables users to search for orthologs using either
nucleotide or amino-acid queries from expressed sequence data. The user-friendly nature
of this system was a primary motivator in the development of our tools and workflows, as
the key objective is for our data and methods to be easily accessible both within and
outside the field of phylogenetics.
Galaxy Implementation
We implemented every step of our phylogenomics workflow in Galaxy, an open source
bioinformatics platform. This required the development of many new wrappers and
scripts, including custom wrappers for EvolMap, HaMStR, MUSCLE, AliScore, and
RAxML. We have been compiling tools into Osiris, a suite of tools to conduct all stages
of phylogenetic analysis in Galaxy. Many of these tools utilize phytab format, a tabular
text file containing the species name, ortholog name, gene ID and sequence data in
separate columns (26). Phytab files represent an improvement over previous approaches
as they can be easily edited in common spreadsheet programs, and they contain all the
data partitions (in this case all different orthologs) in a single file, making concatenation
much easier and manageable within Galaxy. Furthermore, phytab files allow for
significant parallelization within Galaxy as each ortholog can be more easily analyzed on
different processors, thereby significantly accelerating multiple sequence alignment and
gene tree estimation.
Open Access to all Data and Tools
The raw data resulting from Illumina transcriptome sequencing in this study (including
alignments and trees) have been made available as a Bioproject on the NCBI SRA
archive (see Table S1 for accessions). These resources will help inform both future
phylogenomic and experimental ecology studies. Galaxy workflows will be provided
upon request to MAA. The user-friendly nature of this system was a primary motivator in
the development of our tools and workflows, as the key objective is for our data and
methods to be easily accessible both within and outside the field of phylogenetics. All
tools used are available for download from bitbucket as part of the Osiris package
developed at UCSB (26). Both a blog and a wiki page have been setup in order to expand
on the methodology and promote discussion and development of the tools. (http://osirisphylogenetics.blogspot.com).
Phylotranscriptomics in Ecology
We present the most comprehensive phylogenetic framework ever used to test
phylogenetic community structure and phylogenetic signal in ecology (Figure 1). With
our phylogeny in hand, freshwater green algae offer a number of advantages as a
laboratory system for experimental phylogenetic ecology. Freshwater green algae
comprise one of the most diverse groups of primary producers, can be easily and rapidly
grown under laboratory conditions and can be ordered from culture centers. Many of the
freshwater ecosystems they inhabit are highly threatened and in need of protection and
urgent conservation strategies (27). Therefore, results from experimental manipulations
of freshwater green algae can potentially be used to directly inform policy (28). Other
than green algae, diverse and ecologically significant primary producers include the
Chromalveolata and the Cyanobacteria (29). Both lineages are critically important from
an ecological and evolutionary perspective, yet their phylogenetic relationships remain
largely understudied, primarily due to the sheer scale of diversity (30-32). Bacteria have
been successfully used in many recent experimental ecology studies (33-35), however,
the species selected for such studies often do not occupy major ecological roles. It is
therefore critical that microcosm experiments aiming to inform global ecological
problems should use species that are ecologically relevant and widespread in natural
ecosystems.
Our phylogeny of culturable green algae illustrates how phylotranscriptomics – the
phylogenetic analysis of transcriptome data - is emerging as a valuable tool for
determining evolutionary relationships (36). The advent of de novo assembly of shortread, high throughput cDNA sequence data (trinity) allows collection and use of vast
amounts of data, even when genomic information is not available for a particular group.
In addition to phylotranscriptomics, another genome reduction technique (so called
because a portion of the genome is sequenced) is target enrichment, where scientists use
probes to enrich gene targets before conducting high throughput sequencing.
Phylotranscriptomics and target enrichment each have advantages and disadvantages.
One potential disadvantage of phylotranscriptomics is that it requires RNA and therefore
very well preserved tissue. Target enrichment uses genomic DNA, which is more easily
preserved than RNA. Yet an advantage of phylotranscriptomics is that collecting data
requires no a priori genomic knowledge, whereas target enrichment uses fully sequenced
genomes to find conserved genetic regions to be sequenced. So far, target enrichment has
mainly been used in groups like vertebrates with a relatively large number of fully
sequenced genomes relative to diversity (37-40). It remains to be seen whether groups as
divergent as green algae could benefit from target enrichment phylogenetics. An
interesting approach could be to blend the two approaches. For example, the
transcriptomic data we present here could be used to design target enrichment probes
across green algae.
Few previous phylotranscriptomic studies have utilized extra-nuclear genes for
phylogenetic analyses, yet we found that chloroplast data added important phylogenetic
signal to the transcriptomic data from nuclear genes. One reason extra-nuclear genes may
not have been used is that many previous phylotranscriptomic studies investigated deep
relationships of animals. In animals, mitochondrial genes probably evolve too quickly to
provide reliable phylogenetic signal for very ancient relationships (36). However,
chloroplasts have been successfully used to infer phylogenetic relationships of very
distantly related algal species, on their own (17) and in combination with nuclear and
mitochondrial data (41, 42). Our approach of combining nuclear and chloroplast data has
yielded a vast amount of orthologous genes compared to previous studies of green algal
phylogenomics (4, 43), and our results suggest that partitioned analyses of nuclear and
extra-nuclear genes can offer valuable phylogenetic insights.
PD Transformations & Variance
As the number of studies showing no signal of PD has grown, two statistical arguments
have emerged accounting for the lack of signal. The first suggests that the use of linear
models to detect non-linear trends is inappropriate (44). The second is variance in the
response variable increases with increasing PD, and the signal of PD is 'masked' by the
variance and can't be detected with linear models (45). We address the first issue by
transforming PD values and the second by testing variance in our ecological variables.
We transformed the PD values in order to test whether our lack of correlations was due to
the use of linear models attempting to detect non-linear trends. In order to address this
issue, we used a square root transformation and a power function (y = ax^b). The power
function can fit any monotonically increasing (b > 0) or decreasing (b < 0) function, and
can range from linear (b = 1) to decelerating (0 < b < 1) to accelerating (b > 1). So for
each of the published ecological studies, we took the log of PD, the log of the response
variable (e.g., Relative densities, NDs and FDs, and sensitivity to invasion), and fit the
parameter b in order to obtain confidence intervals. If b is not significantly different than
1, then the data relationships are linear. All of our results from these tests confirm that
there is no relationship, neither linear nor non-linear, relating ecological variables to PD.
We used the bruesch pagan test to test for variance (heteroscedasticity) in the published
ecological studies. Heteroscedasticity became nonsignificant after transforming the data
(both when log transforming the dependent variable alone to make it more normally
distributed, or when linearizing with log-log plots). After both types of transformations,
all linear models were non-significant.
Table S2 – Phylogenetic workflow in Galaxy
Steps
1
2
3
4
5
6
Process
Multiple sequence alignment of each ortholog
independently in Phytab format
Gene tree estimation for each ortholog
Identification of long branch artifacts
Removal of long branch artifacts
Repeat of steps 1-4
Final multiple sequence alignment
7
8
Concatenation of final dataset
Final ML tree estimation
Tools
Phytab Muscle
Phytab RAxML
Long Branch Finder
Prune Phytab
Phytab Muscle &
Phytab Aliscore/Alicut
Phylocatenator
RAxML
Table S3 – Phylogenetic statistics and matrix density
Phylocatenator
Settings
1 gene per species
1 species per gene
10bps min
5 genes per species
5 species per gene
10bps min
10 genes per species
10 species per gene
10bps min
10 genes per species
20 species per gene
10bps min
10 genes per species
20 species per gene
100bps min
Number of
Genes
1638
Aligned
bps
218732
Alignment
Patterns
171333
Mean ML
Bootstrap %
25.2
1600
213465
169219
28.3
1479
196184
156781
39.3
464
38853
34492
47.2
119
19949
16978
79.5
Figure S1: A simplified representation of the EvolMAP and Hamstr workflow for
creating customized orthologs. The process starts in EvolMAP with a species tree-based
clustering method that joins all-to-all symmetrical similarity comparisons of multiple
gene sets. EvolMAP allows users to easily extract sets of orthologous genes (core
orthologs) for any taxonomic group for which whole genomes are available. Orthologous
genes are then aligned, and used to build HMMs. HaMStR, a Hidden Markov Model
(HMM) based search tool is used to screen transcriptome sequence data for the presence
of putative orthologs in a predefined set of genes. Subsequently, all data that meets the
blast reciprocity criterion in HaMStR is retained and used in downstream phylogenetic
workflows.
Figure S2: Here we show a graphical representation of Galaxy workflows used to
analyze RNA-seq, whole genomes, ESTs and chloroplast data. (A) depicts the process of
creating HMMs from genome sequence data; (B) depicts the analysis of RNA-seq up to
assembly; (C) shows the assembly of raw EST data from GenBank; (D) is raw
chloroplast genome data; (E) involves the search for orthologs in raw data using HMMs
built in the previous steps; (F) orthologs are aligned and artifacts removed; (G) All data is
concatenated and run through RAxML.
Figure S3: The resulting best scoring maximum likelihood phylogeny from the RAxML
analyses. Bootstrap support values exceeding 70% likelihood are depicted with black
circles. Taxa with uncertain taxonomic placement (incertae sedis) are depicted with black
squares.
Figure S4 – Combined Phylogenomic + Genbank tree
Botryococcus
Characium
Protoderma
Ulothrix
Tetraselmis
Chlorokybus
Mougeotia
Mesotaenium
Zygnema
Roya
Closterium
Staurastrum
Docidium
Micrasterias
Cosmarium
Euastrum
Xanthidium
Staurodesmus
Pleurotaenium
Spondylosium
Teilingia
Spirogyra
Netrium
Elakatothrix
Micromonas
Ostreococcus
Tetrastrum
Planctonema
Coccomyxa
Eremosphaera
Crucigeniella
Lagerheimia
Oocystis
Dictyosphaerium
Nannochloris
Gloeotila
Hegewaldia
Closteriopsis
Chlorella
Actinastrum
Mucidosphaerium
Micractinium
Oedogonium
Golenkinia
Asterococcus
Gloeocystis
Chlamydocapsa
Phacotus
Polytoma
Chlorococcum
Platydorina
Chlamydomonas
Gonium
Pandorina
Eudorina
Volvox
Trochiscia
Treubaria
Fusola
Gloeomonas
Carteria
Spermatozopsis
Desmodesmus
Acutodesmus
Scenedesmus
Crucigenia
Hariotina
Westella
Dimorphococcus
Coelastrum
Polyedriopsis
Botryosphaerella
Sorastrum
Stauridium
Hydrodictyon
Pediastrum
Pseudopediastrum
Tetraedron
Planktosphaeria
Radiococcus
Follicularia
Sphaerocystis
Microspora
Raphidocelis
Selenastrum
Kirchneriella
Ankistrodesmus
Monoraphidium
Quadrigula
Schroederia
Stigeoclonium
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