DOI: 10.5061/dryad.6djh9w13b
title: Genome size drives morphological evolution in organ-specific ways

description: Morphogenesis is an emergent property of biochemical and cellular
 interactions during development. Genome size and the correlated trait of
 cell size can influence these interactions through effects on
 developmental rate and tissue geometry, ultimately driving the evolution
 of morphology. We tested whether variation in genome and body size is
 related to morphological variation in the heart and liver using nine
 species of the salamander genus Plethodon (genome sizes
 29–67 gigabases). Our results show that overall organ size is a function
 of body size, whereas tissue structure changes dramatically with
 evolutionary increases in genome size. In the heart, increased genome size
 is correlated with a reduction of myocardia in the ventricle, yielding
 proportionally less force–producing mass and
 greater intertrabecular space. In the liver, increased genome
 size is correlated with fewer and larger vascular structures, positioning
 hepatocytes farther from the circulatory vessels that transport key
 metabolites. Although these structural changes should have obvious impacts
 on organ function, their effects on organismal performance and fitness may
 be negligible because low metabolic rates in salamanders relax selective
 pressure on function of key metabolic organs. Overall, this study suggests
 large genome and cell size influence the developmental systems involved in
 heart and liver morphogenesis.
description: <strong><em>Animal collection­­­:
 </em></strong>We collected five adult individuals
 of <em>P. cinereus</em>, <em>P.
 cylindraceus</em>, <em>P</em><em>.
 dunni</em>,<em> P. glutinosus</em>, <em>P.
 idahoensis</em>,<em> P. metcalfi</em>, <em>P.
 montanus</em>,<em> P. vandykei</em>, and<em> P.
 vehiculum</em>. Permit IDs and locality data can be found in the
 supplemental data. Salamanders were collected and euthanized in neutral
 buffered (pH 7) 1% MS-222, fixed in buffered formalin, and transferred
 through a graded series of ethanol (10%, 30%, 50%, 70%) before storage in
 70% ethanol. Due to the rarity of some species included in this study, we
 used the same specimens for the diceCT and histological analyses. The
 protocols for animal research, husbandry, and euthanasia were approved by
 the Institutional Animal Care and Use Committee. 
 <strong><em>diceCT data generation and processing:
 </em></strong>We used diceCT to measure liver and heart
 volumes (Gignac et al., 2016). I<sub>2</sub>KI staining can
 cause varying degrees of tissue shrinkage, but these artifacts can be
 minimized by using low concentrations of I<sub>2</sub>KI and
 shorter staining periods (Vickerton et al., 2013; Baverstock et al., 2013;
 Hedrick et al., 2018). To minimize such shrinkage, we used a 1%
 I<sub>2</sub>KI solution and incubated the specimens for 2
 days, which was shown to produce the smallest degree of tissue shrinkage
 (Vickerton et al., 2013). After scanning, the I<sub>2</sub>KI
 was rinsed out using several changes of 70% ethanol and specimens were
 stored in 70% ethanol. In addition, all specimens were treated uniformly
 to minimize the risk of any potential I<sub>2</sub>KI-related
 artifacts or shrinkage introducing noise or bias into subsequent
 histological analyses. Following these precautions, we did not observe any
 significant signs of tissue shrinkage across organs and
 specimens.  Specimens were scanned twice using a Bruker
 SkyScan 1173 at the Karel F. Liem Bioimaging Center, Friday Harbor
 Laboratories, University of Washington. Scans were set to 85 kV and 90 uA
 with a 1 mm aluminum filter to reduce beam hardening. We first produced
 full body scans to measure liver volumes at a resolution ranging from
 14.9–17 μm, depending
 on the size of the specimens. We then produced higher resolution scans for
 the heart, which were scanned at a resolution ranging from 7.1 to 9.9
 μm. diceCT scans were reconstructed using NRecon (Bruker,
 2005–2011) following standard operating procedures including optimal x/y
 alignment, ring artifact reduction, beam hardening correction, and a
 post-alignment. Data visualization and analysis were accomplished using
 3DSlicer (Fedorov et al., 2012). Liver and heart ventricle volumes were
 calculated through segmentation of the liver and heart from each
 specimen. <strong><em>Organ measurement:
 </em></strong>Snout–vent lengths were measured to the nearest
 0.01 mm for each individual using digital calipers. The hearts and livers
 were then excised from specimens and were embedded in plastic following
 standard protocols (Humason, 1962).<strong> </strong>Tissues
 were sectioned at 4 µm and stained with hematoxylin for 4 minutes and
 toluidine for 3 minutes. Sections were mounted and then visualized using a
 compound microscope. Images used in the analysis were minimally edited to
 remove blood cells that obstructed vascular structures or
 the intertrabecular space of the ventricular myocardium. Five images were
 taken (one each from 5 different histological sections) at 20x
 magnification for each individual and a mean value was calculated for each
 morphological trait per individual. We decided to collect morphological
 data from 5 images after finding no significant differences when the data
 were collected from 3, 5, or 10 images. Each image was converted to
 greyscale and a thresholding method was used to collect the morphometric
 data. ImageJ was used for all image processing and analysis (Schneider et
 al., 2012). Amphibian livers are primarily comprised of
 hepatic tissue that is permeated by the vasculature (Akiyoshi and Inoue,
 2012). Liver vascular structures consist of hepatic arteries that provide
 oxygen, portal veins that bring nutrients and toxins to the liver, and
 sinusoids, which are specialized capillaries where oxygen-rich blood from
 hepatic arteries and nutrient-rich blood from portal veins mix (Elias and
 Bengelsdorf, 1952). Liver tissue is generally arranged into many hepatic
 lobules that are centered by portal triads – an arrangement of hepatic
 arteries, portal veins, and bile ducts (Elias and Bengelsdorf, 1952). The
 network of sinusoids gives the hepatic tissue a cord-like appearance in
 most vertebrate taxa, with hepatocytes forming cords that are 1–2 cells
 thick; this morphology increases the surface area of each hepatocyte that
 is in contact with circulating blood (Elias and Bengelsdorf, 1952).
 However, some species of salamanders have a many-cell thick arrangement of
 hepatic cords (Akioyshi and Inoue, 2012). For the liver, we measured the
 total area of each histological section that was comprised of tissue
 (primarily hepatocytes) versus vascular openings and the number and size
 of distinct vasculature (sinusoids, veins, arteries) (Fig. S1). Twenty
 nuclei and cells were also measured for each individual to collect data on
 hepatocyte nuclear and cell area. Amphibians have a
 single, thin-walled ventricle that has a central chamber surrounded by a
 highly trabeculated network of myocardium, which is a characteristic of
 ectotherms (Stephenson et al., 2017). Heart morphology
 in <em>Plethodon </em>also reflects the lack of lungs in the
 family Plethodontidae, which has been accompanied by a loss of complete
 atrial septation (Lewis and Hanken, 2017). For the heart ventricles, we
 measured the myocardial area in the ventricle walls versus intertrabecular
 space in each histological section (Fig. S1). We focused on this because
 the trabeculated myocardium makes it difficult to define the edges of the
 ventricle chamber. We did not measure any characteristics of the atria
 because they lack distinct internal structure and their elastic nature
 made accurate volumetric measurements impossible. 
 <strong><em>Genome size measurement:
 </em></strong>Genome size was measured using the
 Feulgen-staining method on fixed erythrocytes following the protocol of
 Sessions and Larson (1987). <em>Ambystoma mexicanum</em> (32
 Gb) was used as a standard to calculate the genome sizes of the other
 species. The <em>A. mexicanum </em>were acquired from
 the <em>Ambystoma</em>Genetic Stock Center at the University
 of Kentucky. Erythrocytes were extracted from
 the <em>Plethodon </em>and <em>Ambystoma</em>specimens fixed in neutral-buffered (pH 7) formalin, and transferred to microscope slides to produce blood smears. We collected blood smears from 3–5 individuals per species. The cells were hydrated for 3 minutes in distilled water, permeabilized in 5 N HCl for 20 minutes at 20°C, and then rinsed three times in distilled water. Nuclei were stained with Schiff’s reagent for 90 minutes at 20°C, destained in 0.5% sodium metabisulfite three times for 5 minutes each, and then rinsed in distilled water three times. The stained cells were dehydrated in a graded series of 70%, 95%, and 100% ethanol, dried, and mounted. Each staining run included a slide of <em>Ambystoma</em> cells as the standard. We photographed 2–12 (x̅ = 5.5) nuclei per individual under 100x, and the integrated optic densities (IOD) were measured using IMAGE PRO software (Media Cybernetics, Rockville, Maryland, USA). Genome sizes were calculated by comparing the average IODs of the experimental species to the IOD of the standard. Nuclear areas were measured for each erythrocyte using IMAGE PRO software, and hepatocyte nuclear and cell areas were also calculated for 20 cells from each of 4–5 individuals using IMAGE PRO. We tested for the predicted correlations between genome size and nuclear area and cell area using linear regression. <strong><em>Phylogeny: </em></strong>We estimated the phylogenetic relationships among the 9 species of <em>Plethodon </em>used<em> </em>in this study to account for phylogenetic non-independence in our analyses. DNA sequences for the mtDNA gene cytb and the nuclear gene Rag1 were obtained from NCBI (https://www.ncbi.nlm.nih.gov/genbank; Table S1). The cytb<em> </em>and Rag1sequences were aligned independently using MUSCLE in MEGA v7 with default parameters and trimmed to 629 and 1,467 basepairs, respectively (Edgar, 2004; Kumar et al., 2016). We applied a codon-specific nucleotide substitution model determined by the best-fit models using AICc with PartitionFinder 2 using the “greedy” search algorithm (Lanfear et al., 2017). We applied the following model scheme: Rag1<em> </em>codon position 1, F81; Rag1<em> </em>codon position 2, HKY + I; Rag1<em> </em>codon position 3, HKY; cytb<em> </em>codon positions 1–2, GTR + G; cytb<em> </em>codon position 3, HKY + G. The phylogeny was estimated using Bayesian inference with MrBayes v3.2.5 (Ronquist et al., 2012). The analysis ran with four chains (3 heated, 1 cold) for 10 million generations with sampling occurring every 1,000 and the first 10% of the sampled trees discarded as burnin. <strong><em>Data analysis: </em></strong>We log-transformed all variables to account for non-normal distributions and created phylogenetic generalized least squares (PGLS) models that simultaneously estimated Pagel’s lambda (l), a measure of phylogenetic signal (Revell, 2010). Each PGLS model included one of the morphological traits as a response and genome size and SVL as the predictor variables to test if organ morphology is correlated with genome size and/or SVL while accounting for phylogeny. For each species, we also calculated the biological size index (BSI) ¾ a relative measure of the total number of cells comprising an organism that is based on organism size and cell size ¾ by dividing the mean SVL by the square-root of genome size (Hanken and Wake, 1993; Decena-Segarra et al., 2020). Our response variables were liver size, ventricle size, total area comprised of muscle in the ventricle, number of vascular structures in the liver, average size of the vascular structures in the liver, and the total area comprised of hepatic tissues in the liver. The PGLS analyses were conducted using R v 3.4.2 and the packages <em>caper </em>and <em>nlme</em> (Orne et al., 2013; R core team, 2016; Pinheiro et al., 2021). We applied a Brownian motion model of evolution to all variables, and we applied a Benjamini–Hochberg false discovery rate correction to account for multiple testing (Benjamini and Hochberg, 2000). We visualized the magnitude and direction of changes in genome size across the 9 species of <em>Plethodon </em>in the study using the contMap function in the R package <em>phytools </em>on the estimated topology (Revell, 2012).
description: Four data files. Plethodon_phylogeny.nwk is a
 newick file of the 16S/cytb phylogeny used for the PGLS
 analyses. Genome_size.csv contains the mean IOD
 measurement, genome size measurement, and the mean area of erythrocyte
 nuclei for each specimen. Morphology_data_means.csv
 contains the means of each morphological and histological trait for all
 species. Raw_data.csv contains the raw morphological
 and histological measurements for all species.