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Genome size drives morphological evolution in organ-specific ways

Citation

Itgen, Michael (2022), Genome size drives morphological evolution in organ-specific ways, Dryad, Dataset, https://doi.org/10.5061/dryad.6djh9w13b

Abstract

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.

Methods

Animal collection­­­: We collected five adult individuals of P. cinereusP. cylindraceusP. dunni, P. glutinosusP. idahoensis, P. metcalfiP. montanus, P. vandykei, and P. vehiculum. 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. 

diceCT data generation and processing: We used diceCT to measure liver and heart volumes (Gignac et al., 2016). I2KI staining can cause varying degrees of tissue shrinkage, but these artifacts can be minimized by using low concentrations of I2KI and shorter staining periods (Vickerton et al., 2013; Baverstock et al., 2013; Hedrick et al., 2018). To minimize such shrinkage, we used a 1% I2KI 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 I2KI 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 I2KI-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.

Organ measurement: 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). 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 Plethodon 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. 

Genome size measurement: Genome size was measured using the Feulgen-staining method on fixed erythrocytes following the protocol of Sessions and Larson (1987). Ambystoma mexicanum (32 Gb) was used as a standard to calculate the genome sizes of the other species. The A. mexicanum were acquired from the AmbystomaGenetic Stock Center at the University of Kentucky. Erythrocytes were extracted from the Plethodon and Ambystomaspecimens 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 Ambystoma 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.

Phylogeny: We estimated the phylogenetic relationships among the 9 species of Plethodon used 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 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 codon position 1, F81; Rag1 codon position 2, HKY + I; Rag1 codon position 3, HKY; cytb codon positions 1–2, GTR + G; cytb 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.

Data analysis: 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 caper and nlme (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 Plethodon in the study using the contMap function in the R package phytools on the estimated topology (Revell, 2012).

Usage Notes

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.