University of Nevada, Reno
Understanding the Ecological Consequences of Phytochemical Diversity Through
Molecular Networking Approaches
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemistry
By
Kaitlin M. McDermott
Dr. Christopher S. Jeffrey/Dissertation Advisor
May, 2019
THE GRADUATE SCHOOL
We recommend that the dissertation prepared under our supervision by
KAITLIN M. MCDERMOTT
Entitled
Understanding the Ecological Consequences of Phytochemical Diversity Through Molecular Networking Approaches
be accepted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Christopher S. Jeffrey, Ph. D., Advisor
Vincent J. Catalano, Ph. D., Committee Member
Craig D. Dodson, Ph. D., Committee Member
Lora A. Richards, Ph. D., Committee Member
Marjorie D. Matocq, Ph. D., Graduate School Representative
David W. Zeh, Ph. D., Dean, Graduate School
May, 2019 i
Abstract
Secondary metabolites are important mediators of a variety of biotic and abiotic interactions between plants and their surrounding environment. Many fully or partially characterized plants produce phytochemical mixtures that serve to protect the plant, including toxicity to herbivores. Despite the observed toxicity of secondary metabolites, some herbivores successfully specialize on toxic diets. Traditional methods for discovering/evaluating biologically active secondary metabolites in the context of organismal interactions have been targeted towards a specific set of compounds or compound classes. For partially characterized or uncharacterized plants, non-targeted metabolomic approaches are more desirable as they offer a more global evaluation of an organism’s metabolome, accounting for minor components and synergistic interactions.
Current network approaches developed for large genomic datasets can be adapted to suit metabolomic datasets to separate significant trends from noise.
Piper is a hyper-diverse plant genus that is well-known for its phytochemical diversity and chemically-mediated plant-insect interactions. The evolution of phytochemical diversity across a subset of Piper species in the Radula clade was investigated to test different phytochemical diversity hypotheses, specifically the co- evolutionary arms race and screening hypotheses. A cosine similarity scores network analysis revealed phytochemical diversity does not parallel species diversity. However, a weighted gene co-expression network analysis (WGCNA) suggested certain structural features and the corresponding biosynthetic pathways are conserved across the phylogeny.
These results support the screening hypothesis where plants maintain high phytochemical diversity to increase their probability of producing a potent compound or precursor. ii
The consequences of phytochemical diversity can also be applied to the mammalian herbivores Neotoma bryanti and N. lepida. N. bryanti and N. lepida are two closely related, yet geographically distinct, species whose habitats converge in a hybrid zone in Kelso
Valley, CA. It is hypothesized that N. bryanti and N. lepida remain distinct by specializing on different toxic diets. A non-targeted metabolomic approach revealed N. bryanti and N. lepida have unique metabolic responses both when consuming their habitat-specific diets and when consuming the diet in the adjacent habitat. These findings suggest there are different metabolic strategies employed by the different species when consuming toxins they are not accustomed to in their normal diet. Furthermore, network analyses revealed the metabolic responses of these two species remain distinct despite seasonal changes in their diets. In addition, we found evidence of seasonal variation in gut microbial communities between N. bryanti and N. lepida, providing strong support that the gut microbiome is critically involved in the detoxification of various plant toxins encountered by these two species.
Finally, we explored the consequences of milkweed (Asclepias) phytochemical diversity on the monarch butterfly (Danaus plexippus). Milkweed species are known to produce toxic cardiac glycosides, known as cardenolides, which have demonstrated anti- parasitic effects for the monarch butterfly. WGCNA of non-targeted metabolomic data resulted in the quantification of the chemotypes of two milkweed species, A. curassavica and A. incarnata. Careful inspection of these chemotypes revealed that flavonoids, in addition to cardenolides, may be an integral part of the monarch’s immune response.
iii
Dedication
For Ehren and Austin. Thank you for your unwavering love and faith in me. Without you,
this would not have been possible.
iv
Acknowledgements
Thank you to my advisor, Dr. Chris Jeffrey. Your passion and enthusiasm for science is magnetic. Thank you for taking a chance on me as I had no background in organic chemistry or ecology. The countless opportunities you provided me with and hours spent helping me develop as a scientist and a person did not go unnoticed. Thank you for your incredible compassion, for creating a safe environment to discuss anything, and for making me and my opinion feel valued when I was lost. Your encouragement in both my academic and personal endeavors has been invaluable. While it may seem insignificant, I want to thank you also for instilling in me a passion for travel as this is something I will carry with me for the rest of my life. You are exceptional in every way and it has been a sincere pleasure to work with you.
I would also like to thank the members of my doctoral committee, Dr. Vincent
Catalano, Dr. Craig Dodson, Dr. Lora Richards, and Dr. Marjorie Matocq. Thank you for all of your time spent helping me become a better scientist and for your invaluable feedback on my documents and oral presentations. To Dr. Catalano, thank you for your unwavering support and humor when I was a new graduate student, and for your continued guidance throughout my graduate career. To Dr. Dodson, thank you for working with me when I first joined Dr. Jeffrey’s group and was trying to catch up. Thank you also for the support you have offered both in my academic career and personal life! To Dr. Richards, thank you for being an incredible role model and for bridging many knowledge gaps for me between chemistry and ecology. I will always appreciate your selflessness and the way you readily lend your emotional support. I think I speak for everyone when I say thank you for bringing a lightheartedness to our collaboration! Lastly, to Dr. Matocq, thank you for sharing in v your wonderful work with woodrats and for the care and understanding you showed me many, many times.
Thank you to Dr. Stephen Spain for your seemingly endless patience in helping me with instrumentation, but more importantly, thank you for supporting me in my personal life. Your compassion and kindness while I was pregnant and when I became a new parent meant the entire world to me. Thank you for the many long, life-giving talks and advice about children, parenthood, graduate school, and life.
To Dr. Sarah Cummings. It is hard to know where to begin. Thank you for being a fantastic teaching and personal role model. The empathy and kindness with which you teach your students is inspiring, and you are the reason I will pursue a career in teaching.
Thank you for dedicating much of your time to helping me grow as a teaching assistant, and for giving me hands-on experience guest lecturing for much larger class sizes. You have empowered me countless times as a teacher and a person, and I cannot express the depth of my gratitude for who you are and what you have done for me.
Thank you to the Jeffrey group lab members, past and present, for the laughs and the memories. To Casey especially, my graduate career would have been drastically different without you. Thank you for your patience, the long talks, the many hours (and pages) spent on feedback, and for being my sounding board. You are a fantastic lab mate, but I could truly never thank you enough for your friendship. And to Andrea Glassmire, thank you for being an incredible, supportive friend and lab mate from the very first day I met you. You always encouraged me to press on with your consistent warmth and compassion. vi
To my parents and especially my mom, who may have missed her calling as a motivational speaker. Thank you for the daily pep talks, for being the best listener of all time, and for being tied with Ehren as my greatest cheerleader. To my dad. Thank you for taking a genuine interest in my research and reminding me of my strength. And to my
Nanna, you are the greatest UNR alumna there ever was and I’m proud to have attended the same school as you.
To my husband, Ehren. There are no words to describe how much I value your unrivaled love and support, but I will try. Thank you for going on this journey with me and believing in me when I couldn’t believe in myself. Thank you for being my audience before oral presentations, my practice student before guest lectures, and my excel helper. Thank you for holding me accountable every day and for being part of every high and every low.
Your work ethic and decision to embrace change inspires me daily. Finally, to my sweet boy, Austin, thank you for showing me what matters in life. Your sweet arrival into this world gave me the extra push I needed to complete this dissertation.
vii
Table of Contents
Abstract i
Dedication iii
Acknowledgements iv
Table of Contents vii
List of Tables xi
List of Figures xii
Chapter 1: Introduction
1.1 Chemical ecology and metabolomics 1
1.2 Molecular networking for metabolomic datasets 3
1.3 Cosine similarity score (dot product) networks 3
1.4 Weighted Gene Co-Expression Network Analysis (WGCNA) 6
1.5 References 8
Chapter 2: Cross-taxa Comparisons of Plant Secondary Metabolites in
the Radula Clade
2.1 Introduction 14
2.2 Methods 17
Sample collection 17
Plant metabolomics 19
Cosine similarity scores network 20
Weighted Gene Co-Expression Network Analysis (WGCNA) 21
2.3 Results 21 viii
Cosine similarity scores network 21
WGCNA 23
Patterns in modules mapped onto Radula phylogeny 26
2.4 Discussion 28
2.5 Conclusions and future directions 30
2.6 References 31
Chapter 3: Small Mammal Metabolism Across a Sharp
Environmental Gradient
3.1 Introduction 36
3.2 Methods 41
Sample collection and diet determination 41
Fecal metabolomics 41
3.3 Results 42
Plant diet determination 42
Fecal metabolomics 44
Network analysis 44
3.4 Discussion 49
3.5 Conclusions and future directions 57
3.6 Supplemental material 58
3.7 References 62
Chapter 4: Seasonal Changes in Neotoma spp. Metabolomes ix
Across the Chemoscape of a Sharp Environmental Gradient
4.1 Introduction 71
4.2 Methods 74
Sample collection 74
Fecal metabolomics 74
Cosine similarity scores network 75
Weighted Gene Co-Expression Network Analysis (WGCNA) 75
4.3 Results 76
Sample collection 76
Cosine similarity scores network 76
WGCNA 85
4.4 Discussion 88
4.5 Conclusions and future directions 91
4.6 References 92
Chapter 5: Quantifying the Chemotypes of Two Toxic Asclepias spp.
and Their Effects on the Specialist Herbivore, Danaus
plexippus
5.1 Introduction 97
5.2 Methods 99
Sample collection 99
Plant metabolomics 100
Cosine similarity scores network 100 x
WGCNA 101
5.3 Results 101
Cosine similarity scores network 102
WGCNA 105
5.4 Discussion 111
5.5 Conclusions and future directions 119
5.6 References 120
Chapter 6: Conclusions and Future Work
6.1 Conclusions and future work 128
Phytochemical diversity is labile 128
Toxic phytochemicals maintain separation of two mammalian 129
herbivores
Non-targeted metabolomic approach suggests additional 130
phytochemical involved in insect herbivore immune response
6.2 References 131
xi
List of Tables
Table 2.2.1. Collection Information for Each Radula Sample Included in this 17
Study
Table 3.1.1. Compound Classes Identified in Rhamnus and Prunus. 38
Table 3.3.1. Proportion of Rhamnus or Prunus Plant DNA Found In Each 43
Individual’s Feces.
Table 3.4.1. Common Positive Adducts of Cyanogenic Glycosides and 53
Mandelonitrile.
Table 3.4.2. Shared Masses in Different Clusters. 56
Table 3.6.1. Hill Metabolites m/z Labels. 58
Table 3.6.2. Flats Metabolites m/z Labels. 59
Table 3.6.3. Ubiquitous Metabolites m/z Labels. 60
Table 4.3.1. Samples Collected from Each Habitat in Each Month. 76
Table 5.2.1. Plant Sample Distribution. 100
Table 5.3.1. Module Chemical Shifts. 108
Table 5.3.2. Description of Ecological Response Variables. 109
Table 5.4.1. Summary Table of Significant Module Correlations. 117
xii
Lists of Figures
Figure 1.3.1. Cosine similarity score network workflow. 5
Figure 1.4.1. WGCNA workflow. 7
Figure 2.3.1. Cosine similarity scores network of 1H-NMR data from all 22
Radula samples.
Figure 2.3.2. A) Eigenvalue dendrogram showing related modules. 24
B) Eigenvalue adjacency heatmap.
Figure 2.3.3. WGCNA network. Nodes are individual chemical shifts and are 25
colored by their module membership.
Figure 2.3.4. Distribution of module representation across the Radula 27
phylogeny for a subset of annotated modules (darkorange,
grey60, green, blue, yellow, and cyan).
Figure 3.1.1. N. bryanti and N. lepida distribution across the hybrid zone. 37
Figure 3.1.2. Main diet components in the flats and the hill. 38
Figure 3.1.3. Phase I and phase II transformations. 40
Figure 3.3.1. Tripartite network relating plant diet to Neotoma individuals 46
consuming those plants and Neotoma individuals to their
metabolites.
Figure 3.3.2. Simplified tripartite network relating plant diet to Neotoma 49
individuals consuming those plants and Neotoma
individuals to their metabolites.
Figure 3.4.1. Amygdalin biosynthesis, cyanogenesis, and cyanide 51
detoxification in Prunus and other cyanogenic plants. xiii
Figure 3.4.2. Frequency of compound classes occurring in the Rhamnus-Hill 55
cluster and the Prunus-flats cluster.
Figure 4.3.1. Cosine similarity score network where more similar fecal 78
metabolomes are connected by shorter edge lengths. Nodes are
colored by month in which the fecal sample was collected.
Figure 4.3.2. The same cosine similarity score network shown in Figure 4.3.1. 79
Nodes are now colored by habitat in which the fecal sample was
collected.
Figure 4.3.3. 16S rRNA cosine similarity score network where more 81
similar microbial profiles are connected by shorter edge lengths.
Nodes ae colored by month in which the fecal sample was collected.
Figure 4.3.4. 16S rRNA cosine similarity score network where more 82
similar microbial profiles are connected by shorter edge lengths.
Nodes are now colored by habitat in which the fecal sample was
collected.
Figure 4.3.5. trnL cosine similarity score network where more similar trnL 83
profiles are connected by shorter edge lengths. Nodes are
colored by the the month in which the fecal sample was collected.
Figure 4.3.6. trnL cosine similarity score network where more similar trnL 83
profiles are connected by shorter edge lengths. Nodes are now
colored by habitat in which the fecal sample was collected.
Figure 4.3.7. Filtered chemistry cosine similarity score network where more 84
similar fecal metabolomes are connected by shorter edge lengths. xiv
Nodes are colored by the month in which the fecal sample was
collected.
Figure 4.3.8. Filtered chemistry cosine similarity score network where more 85
similar fecal metabolomes are connected by shorter edge lengths.
Nodes are now colored by habitat in which the fecal sample was
collected.
Figure 4.3.9. Module-trait relationships. Rows are module eigengenes, 87
and columns are common gut microbe families and major
diet components.
Figure 5.3.1. Cosine similarity scores network of 1H-NMR data from A. 103
curassavica and A. incarnata plant samples grown at
either ambient or elevated CO2 levels. Nodes in the network
represent plant metabolomes and are colored by plant species.
Figure 5.3.2. Cosine similarity scores network of 1H-NMR data from A. 104
curassavica and A. incarnata plant samples grown at
either ambient or elevated CO2 levels. Nodes in the network
represent plant metabolomes and are colored by CO2 level.
Figure 5.3.3. Cosine similarity scores network of 1H-NMR data from A. 105
curassavica and A. incarnata plant samples grown at
either ambient or elevated CO2 levels. Nodes in the network
represent plant metabolomes and are colored by plant species
and CO2 levels.
Figure 5.3.4. A) HCA of chemical shift values resulting in 9 modules. 106 xv
B) Eigenvalue dendrogram showing related modules.
Figure 5.3.5. Module-trait relationships. Rows are module eigengenes, and 107
traits of interest are plant species and CO2 levels.
Figure 5.3.6. Summary of insect immune response. 110
Figure 5.3.7. Module-trait relationships. Rows are module eigengenes, and 111
columns are ecological response variables of monarchs.
Figure 5.4.1. Overlay of ME spectra for the red, green, and brown modules. 113
Figure 5.4.2. Overlay of ME spectra for the magenta and pink modules. 115
Figure 5.4.3. Comparison of representative A. curassavica and A. incarnata 116
phytochemical mixtures.
1
Chapter 1: Introduction
1.1 Chemical ecology and metabolomics
Ecological interactions have driven the evolution of the complex phytochemical profiles, or mixtures of secondary metabolites, we find in plants today. A distinction is commonly made between primary and secondary metabolites. Primary metabolites are involved in essential functions of an organism such as growth, development, and reproduction.1,2 Secondary metabolites tend to be organism-specific and are often involved in critical interactions between an organism and its environment.3 Plants produce complex mixtures of secondary metabolites that are often toxic and serve as defensive compounds for the plant.1,4 Despite the observed toxicity of many secondary metabolites, herbivores still specialize on various toxic plants.3,5-13 Chemical ecology aims to understand the role of secondary metabolites in mediating these very specific interactions between organisms and the plants they encounter.
Metabolomics is a holistic approach to the comprehensive metabolic characterization of organisms. Together with genomics, transcriptomics, and proteomics, metabolomics reveals information about the actions and relationships of different molecule types within cells, resulting in broad applications to many disciplines of science including organismal responses to environmental stress, infection, and pharmaceutical treatments.14-
18 The process of discovering biologically active secondary metabolites in the context of organismal interactions has traditionally been focused on the targeted quantification of a specified set of compounds or compound classes.19 This approach is often time-consuming as it requires isolation and characterization of the metabolites of interest. Furthermore, 2 removing a secondary metabolite from its natural context could result in overlooking biologically significant metabolites, such as minor compounds, and synergistic interactions.20 Non-targeted metabolomics is a more global approach to the metabolic characterization of an organism as it aims to detect as many compound classes as possible at once. Advantages of a non-targeted metabolomic approach are especially apparent when there is no prior knowledge of metabolite targets within a crude mixture of secondary metabolites. Therefore, this approach allows many metabolic fingerprints to be analyzed and compared at once without the need to characterize a single metabolite.19
Advancements in metabolomic approaches have paralleled considerable advancements in instrumental analysis offering greater sensitivity and enhanced resolution.
Common tools used in metabolomic approaches include nuclear magnetic resonance
(NMR) spectroscopy21-26 and liquid chromatography – mass spectrometry (LC-MS),27-29 each yielding different types of data for further analyses. The use of NMR spectroscopy in metabolomic studies can be advantageous due to its nondestructive and highly reproducible nature, providing a spectral fingerprint of an organism’s metabolome by representing all of its components. However, significant peak overlap between individual metabolites can make pattern recognition across different samples difficult. Alternatively, MS techniques offer increased sensitivity and the ability to separate mixtures into their individual components by coupling to high-performance liquid chromatography (HPLC), thereby separating complex mixtures, such as plant extracts and biofluids, into their individual components, yielding a rough number and relative abundance of compounds present. Many
MS-based metabolomic studies utilize MS/MS which allows users to fragment selected ions, resulting in characteristic fragmentation patterns that serve as spectral fingerprints in 3 a similar way to NMR spectroscopy.30,31 Multi-analysis tools, such as the combination of
NMR spectroscopy and LC-MS, offer invaluable information about all or many components of crude mixtures. The studies in the following chapters take advantage of these two techniques.
1.2 Molecular networking for metabolomic datasets
Due to the tremendous structural diversity of metabolites, metabolomic datasets can quickly become overwhelming in size. As a result, sorting through the intractable mixtures of molecules to extract relevant information remains a primary challenge in this field.
Molecular networking as an organizational and visualization tool for large datasets has become increasingly popular in recent years.32,33 Networks are made of nodes and edges.
Nodes can be whole samples/metabolomic profiles, chemical shifts (NMR data), m/z (MS data), or genes amongst other possibilities. Edges are simply connections between nodes where more similar or related nodes are connected by an edge. It is common to identify clusters in a molecular network which describe groups of highly connected nodes. Types of networks discussed in the following chapters include presence/absence, cosine similarity score (dot product), and Weighted Gene Co-Expression Network Analysis (WGCNA).
1.3 Cosine similarity score (dot product) networks
In 2013, Yang et al.34 described a molecular networking workflow using cosine similarity scores (dot products) with MS/MS data to successfully dereplicate known compounds, thereby simplifying complex biological mixtures and revealing more unique metabolites of potential interest.32 This approach can be broken down into three steps
(Figure 1.3.1). MS/MS data for samples and known “seed” molecules is first collected and tabulated (Figure 1.3.1A). A cosine similarity score is used to measure similarities between 4
MS/MS spectra by indicating differences in relative intensities of all fragment ions and the difference in masses of the parent ions between two spectra (Figure 1.3.1B). Cosine similarity scores range from 0 to 1, where a score of 1 is indicative of identical spectra, and the general cutoff for an edge to be retained in the molecular network is a score between
0.5 and 0.7, although the cutoff value is highly dependent on the dataset and may vary.
Connections in the network are further filtered to include only edges between nodes whose cosine similarity scores appear in the top 10 scores for the other node. Parent ion masses and cosine scores relating matched spectra are then reported as a molecular network visualized using Cytoscape (Figure 1.3.1C). Cytoscape is a tool used for visualizing global relationships within large networks.35 In the network, each node is labeled with a parent ion mass, and the edges connecting the nodes are a reflection of the cosine score between those two nodes (spectra). A thicker edge represents a cosine score close to 1.0, while a thinner edge represents less similar samples. Spectra with similar features cluster around
“seed” spectra, which are MS/MS spectra of known secondary metabolites/natural products and help to elucidate analogue structures more rapidly when present. However, the success of molecular networking is not completely dependent on “seed” spectra. In the absence of these spectra, molecules that display similar fragmentation patterns will still cluster together within a network because of their similarity to one another. The approach described by Yang et al.34 is incredibly powerful and robust for MS/MS datasets.32,36
However, it is not always feasible to collect MS/MS data either due to instrument limitations or cost. Much of the work presented in this dissertation involves adapting this powerful tool to also reveal important trends in 1H-NMR and LC-MS datasets when
MS/MS data is not readily obtainable. 5
A MS/MS Data
Sample
“Seed” Molecules
B C d
m/z 0.7 D m/z a A 0.3 m/z E m/z 0.2 C e m/z F m/z m/z 0.8 0.5 c G B f 0.9 0.7 0.9 b g
Figure 1.3.1.32 Cosine similarity score network workflow. A) MS/MS data is collected for samples and known “seed” molecules. B) Cosine similarity scores are calculated pairwise for all samples. Only connections with scores above the user-defined threshold (ex: 0.5) are retained in the network. C) Connections are visualized in Cytoscape.
6
1.4 Weighted Gene Co-Expression Network Analysis (WGCNA)
WGCNA is a package in R used to identify clusters of highly correlated/co- expressed genes which can then be related to external traits.37,38 WGCNA can be broken down into four steps (Figure 1.4.1). The network is first constructed from an adjacency matrix which determines if two nodes are connected and, if so, how connected they are
(Figure 1.4.1A). In an unweighted network, values in the adjacency matrix are either 0 or
1. A weighted network has the advantage of reporting the strength of the correlation between two genes. The correlation matrix is then transformed continuously using a power adjacency function. Use of soft thresholding is recommended with the power function so the network will satisfy scale-free topology, separating meaningful correlations from noise.
Next, a topological overlap matrix plot coupled with hierarchical cluster analysis (HCA) is used to cluster co-expressed genes together into color-coded modules within the network
(Figure 1.4.1B). Principal component analysis (PCA) is used to assign each sample an eigenvalue for each module detected. Eigenvalues describe how each sample contributes to the formation of each module. To summarize expression profiles in a module, module eigengenes (MEs) are determined for each module. The ME for a module is the sample with the highest eigenvalue for that module and becomes the module’s representative profile. In this way, HCA with module MEs can be used to determine the correlation between two modules (Figure 1.4.1C). These correlations are often visualized with a heatmap. Lastly, MEs can be related to external trait data (Figure 1.4.1D), which allows predictions to be made about how expression of certain genes may be responsible for observed phenotypic changes. WGCNA has become an increasingly important tool with applications across the “omics.”39-41 As with the cosine similarity score networking 7
approach (Section 1.3), WGCNA is adapted in the following chapters to analyze large
metabolomic datasets, relating chemical data to observable changes in phenotype for the
organism being studied.
A C EigenvalueEigenvalue Dendrogram dendrogram 1.0 1 2 3 4 5 6 0.9 MEblack
6 MElightgreen MEtan MElightyellow 3 1 MEdarkturquoise 0 1 0 0 1 0 MEmagenta 4 0.8
MEred 2 1 0 1 0 1 0 MEdarkred
MEturquoise 0.7 MEpurple 2 3 MEsalmon
0 1 0 1 0 0 MEgreenyellow MEbrown MEdarkgreen 5 MEgreen MEpink 4 0 0 1 0 1 1 0.6 MEroyalblue 5 1 1 0 1 0 0 1 0.5
6 0 0 0 1 0 0 MEblue MEgrey60 MEyellow MElightcyan 0.4 0.3 MEcyan MEmidnightblue B D ModuleModule-trait−trait Relationships relationships
0.35 −0.093 MEblack (0.004) (0.5) 0.12 −0.18 MEdarkred (0.3) (0.2) 1 0.21 −0.2 MEred (0.09) (0.1) ClusterCluster Dendrogram Dendrogram 0.24 −0.2 MEgreen (0.06) (0.1) 0.52 −0.2 1.0 MEblue (1e−05) (0.1) 0.43 −0.18 MEyellow (4e−04) (0.2) 0.22 −0.18 MEpurple (0.07) (0.1) 0.5 0.8 0.24 −0.22 MEsalmon (0.06) (0.08) −0.093 0.12 MEdarkturquoise (0.5) (0.4) −0.11 0.16 0.6
MElightyellow (0.4) (0.2) −0.12 0.21 MEtan (0.4) (0.1)
Height −0.13 0.23 MEgreenyellow (0.3) (0.07)
0.4 0
Height 0.078 0.2 MEpink (0.5) (0.1) 0.062 0.15 MEroyalblue (0.6) (0.2) −0.035 0.17
0.2 MElightgreen (0.8) (0.2) 0.046 0.16 MEgrey60 (0.7) (0.2) −0.039 0.18 MElightcyan (0.8) (0.2)
0.0 −0.17 −0.22 −0.5 MEcyan (0.2) (0.09) −0.18 −0.27 MEmidnightblue (0.2) (0.03) −0.086 −0.18 Module as.dist(dissTom) MEmagenta (0.5) (0.2) Module colors fastcluster::hclust (*, "average") Colors −0.051 0.16 MEturquoise (0.7) (0.2) −0.072 −0.091 MEbrown (0.6) (0.5) −0.1 0.11 MEdarkgreen (0.4) (0.4) −1 −0.084 0.28 MEgrey (0.5) (0.03)
Species JulianDateTrait 1 Trait 2 Figure 1.4.1. WGCNA workflow. A) The network is constructed from an adjacency matrix. B) A topological overlap plot with HCA is used to cluster co-expressed genes into modules. C) Module eigengenes (MEs) are determined for each module. HCA is used to determine the correlation between two modules. D) MEs are related to external trait data. 8
Taken together, the work presented in this dissertation aims to modify existing tools to suit other types of datasets, allowing us to make comparisons of complex plant mixtures at unprecedented taxonomic and geographic scales in addition to highlighting the consequences of phytochemical diversity in various systems. In Chapter 2, phytochemical diversity is compared across an entire clade of plants (Radula) to investigate the conservation of biosynthetic pathways and its consequences. Chapter 3 highlights our work exploring toxic diet’s role in the observed habitat separation of two otherwise closely related species of woodrats (Neotoma bryanti and N. lepida). Chapter 4 describes our findings of seasonal variation in the diets and fecal metabolomes of N. bryanti and N. lepida that may be partly mediated by the gut microbiome. Chapter 5 outlines our investigation of the toxic plant genus, milkweed (Asclepias), and its ecological consequences for the specialist monarch butterfly (Danaus plexippus). Lastly, Chapter 6 summarizes how we have used the powerful tools discussed in this chapter to address important ecological questions and future directions for the projects discussed herein.
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14
Chapter 2: Cross-taxa Comparisons of Plant Secondary Metabolites in the Radula
Clade
2.1 Introduction
As discussed in Chapter 1, primary metabolites are crucial to the growth and development of a plant while secondary metabolites mediate a plant’s interaction with its environment. Primary metabolites have traditionally been regarded as more important for a plant’s survival, however, secondary metabolites are now recognized to be of equal importance as they serve to protect plants from fluctuations in both biotic and abiotic factors. Consequently, the diversity of plant secondary metabolites, or phytochemical diversity, is an established driver of insect biodiversity.1-5 Many hypotheses have been put forth to explain how phytochemical diversity has arisen and the precise biological function of these complex mixtures of molecules.1,2,6-10 For example, the co-evolutionary arms race hypothesis predicts greater phytochemical diversity with increased species diversity,2 while the screening hypothesis suggests the ability to maintain high phytochemical diversity increases the chances of a plant containing a potent, defensive compound.10 It is also hypothesized that more closely related plants should produce more similar phytochemical mixtures due to biosynthetic constraints compared to more distantly related plants.11
The hypothesis of phylogenetic conservativism12 has been of particular interest to our group as it is based on the observation that the incredibly diverse set of plant secondary metabolites are constructed from only a few biosynthetic pathways.12,13 Because primary metabolites are essential for growth and development, primary metabolism processes are 15 highly conserved across plant taxa. Any mutations in primary metabolism would be highly unfavorable for the survival of the plant, and therefore these mutations are consistently selected against. Secondary metabolism, however, is highly labile. The incredible number of secondary metabolites produced by different organisms across many taxa is likely due to duplication of primary metabolite processes which allowed mutations in secondary metabolism that persisted as a result of increased catalytic promiscuity.14-19 Secondary metabolites can be broadly categorized as terpenoids, alkaloids, and phenylpropanoids/phenolics depending on their biosynthetic origin.20 Terpenoids are biosynthesized from isopentenyl diphosphate (IPP), alkaloids from amino acids, and phenylpropanoids/phenolics from the shikimate or mevalonate/acetate pathway.20
However, a single biosynthetic pathway can yield many types of secondary metabolites.
For example, the shikimate pathway yields alkaloids, glucosinolates, phenylpropanoids, lignans, flavanoids, and tannins among other secondary metabolites.21 To compound the complexity, phylogenetic conservatism has been observed with a number of compound classes including cardenolides, glucosinolates,12 and alkaloids,22-24 however, there are also instances where seemingly important classes of secondary metabolites are not highly conserved across many taxa.23
Given the incredible diversity of mixtures of secondary metabolites and their importance in mediating a plant’s interaction with its environment, our group has been investigating the evolution of secondary metabolites in the tropical plant genus Piper. Piper has been extensively studied for its many chemically-mediated plant-insect interactions25-
27 and is also well-known for its rich chemical diversity.5 Over 100 Piper species have been characterized chemically, which has resulted in the discovery of 190 alkaloids/amides, 97 16 terpenes, 70 neolignans, 49 lignans, 39 propenylphenols, 18 kavapyrones, 17 chalcones/dihydrochalcones, 16 flavones, 15 steroids, 6 flavanones, 4 piperolides, and 146 compounds that cannot be assigned to one of the major classes of secondary metabolites.28,29 Given Piper’s high phytochemical diversity, it is an appropriate study system for linking biosynthetic pathways to chemical structure to determine which, if any, classes of compounds are conserved across the phylogeny. Careful consideration and annotation of biosynthetic pathways is key to understanding how phytochemical diversity arises and its implications for specific plant-herbivore interactions.
The majority of phytochemical diversity studies have been limited to broader classes of compounds or a handful of molecules that are well-characterized due to the difficulty in quantifying phytochemical diversity in natural systems. These targeted approaches largely ignore synergistic interactions30-32 and favor detection of major components over minor components that may be more significant mediators of complex plant-herbivore interactions. A targeted approach is especially inappropriate when considering different biosynthetic pathways and the incredible diversity of secondary metabolites that can result. Furthermore, comparing different mixtures using a targeted approach is challenging if there is little overlap in phytochemical mixture constituents. A non-targeted metabolomic approach is better suited to accounting for synergy and branching pathways/side products from biosynthetic pathways as it considers the entire metabolic fingerprint of an individual. In collaboration with Dr. Thomas Parchman (UNR-
Biology/EECB), Katie Uckele (UNR-Biology/EECB), Dr. Lora Richards (UNR-
Biology/EECB), and Dr. Lee Dyer (UNR-Biology/EECB), we began our studies by focusing on a subset of Piper species in the Radula clade. A non-targeted metabolomic 17 approach combined with network analyses was used to elucidate patterns in phytochemical mixtures to begin to understand different biosynthetic pathways employed across the Piper phylogeny. Our goals were to: 1) Determine if closely related taxa on the Radula phylogenetic tree produce more similar phytochemical mixtures compared to distantly related taxa, 2) Identify groups of 1H-NMR chemical shift values that may be conserved across the Radula phylogeny, and 3) Begin assigning structural features to modules of chemical shift values that appear to be conserved across the phylogeny.
2.2 Methods
Sample collection: A total of 71 Piper samples from the Radula clade were collected from
Brazil, Costa Rica, Ecuador, Panamá, and Peru to ensure a wide range of geographic, chemical, and phylogenetic variation in the dataset (Table 2.2.1). Samples were air-dried and transported to UNR where we received them for analysis.
Table 2.2.1 Collection Information for Each Radula Sample Included in this Study
Code Species Country 2950 P. baezense Ecuador 2952 P. villalobosense Ecuador 3005 P. barbatum Ecuador 3034 P. maranyonense Ecuador 3036 P. longicaudatum Ecuador 3413 P. culebranum Costa Rica 3415 P. sancti-felicis Costa Rica 3426 P. disparipes Costa Rica 3427 P. cyanophyllum Costa Rica 3430 P. hispidum Costa Rica 3433 P. peracuminatum Costa Rica 3434 P. friedrichsthalii Costa Rica 3438 P. zacatense Costa Rica 3463 P. crassinervium Costa Rica 3470 P. polytrichum Costa Rica 3476 P. hispidum Costa Rica 18
Code Species Country 3482 P. chrysostachyum Costa Rica 3483 P. sp. Costa Rica 3486 P. disparipes Costa Rica 3496 P. hispidum Costa Rica 3499 P. pseudofuligineum Costa Rica 3502 P. colonense Costa Rica 3509 P. hispidum Costa Rica 3523 P. silvivagum Costa Rica 3527 P. culebranum Costa Rica 3531 P. cabagranum Costa Rica 3534 P. acrcteacuminatum Costa Rica 3537 P. hispidum Costa Rica 3541 P. cyphophyllum - 3542 P. xanthostachyum Costa Rica 3543 P. disparipes Costa Rica 3956 P. fimbriulatum - 3959 P. gonocarpum - 3961 P. changuinolanum Panamá 3963 P. dryadanum Panamá 3965 P. polytrichum Panamá 3966 P. hartwegianum - 3967 P. umbellatum - 3972 P. distigmatum - 3974 P. sp. Panamá 3993 P. lucigaudens Panamá 3999 P. cenocladum - 4000 P. euryphyllum - 4005 P. culebranum Panamá 4008 P. tecumense - 4028 P. lucigaudens Panamá 4031 P. villiramulum Panamá 4032 P. colonense Panamá 4039 P. tuberculatum - 4041 P. pseudogaragaranum Panamá 4044 P. amphioxys Panamá 4050 P. latibracteum - 4062 P. peracuminaatum Panamá 4068 P. persubulatum Panamá 4069 P. carrilloanum - 4196 P. holdridgeanum Costa Rica 4393 P. chanchamayanum Perú 4394 P. armatum Perú 4110 P. mollicomum Brazil 4112 P. lagoense Brazil 19
Code Species Country 4113 P. malacophyllum Brazil 4116 P. mosenii Brazil 4117 P. gaudichaudianum Brazil 4122 P. crassinervium Brazil 4126 P. tectoniifolium Brazil 4128 P. chimonanthifolium Brazil 4131 P. goesii Brazil 4132 P. vicosanum Brazil 4142 P. sp. Brazil 4147 P. sp. Brazil YY7 P. schuppii Ecuador
Plant metabolomics: Samples underwent a methanol extraction previously optimized by our lab. Approximately 100 mg of each sample was ground to a fine powder using liquid nitrogen, transferred to a 1-dram vial, and combined with 2 mL of CH3OH. Samples were vortexed for 30 seconds, sonicated for 10 minutes, and the supernatant was decanted and filtered into a pre-weighed 1-dram vial. The remaining leaf material following decantation was combined with another 1 mL of CH3OH, vortexed for 30 seconds, and sonicated for
10 minutes before the filtered supernatants were combined in the tared 1-dram vial. Liquid extracts were dried and concentrated using a Genevac centrifugal evaporator, placed on a hi-vac overnight, and a final extract mass was obtained for each sample.
All samples underwent a deuterium exchange to minimize solvent and water peaks
1 in H-NMR spectra. Dried extracts were first reconstituted in 1 mL methanol-d1, vortexed, and sonicated. Deuterated extracts were dried and concentrated using a Genevac centrifugal evaporator and placed on a hi-vac overnight. Dried deuterated extracts were reconstituted in 500 μL methanol-d1, vortexed, and sonicated. Deuterated extracts were dried and concentrated again using a Genevac centrifugal evaporator and placed on a hi- 20
vac overnight. Dried deuterated extracts were reconstituted in 600 μL methanol-d4 with
0.01% TMS and filtered into NMR tubes for 1H-NMR analysis.
1H-NMR data was collected for each sample on a Varian 400 MHz NMR spectrometer and 1H-NMR data were processed using MestReNova software (Mestrelab
Research, Spain). Each 1H-NMR spectrum was aligned to the residual methanol solvent peak at 3.31 ppm. Spectra were then phase-corrected using the Global method, baseline- corrected using the Whittaker Smoother method, and binned/integrated (Average Sum method) over 0.04 ppm bins ranging from 0.5 – 12.0 ppm in the 1H-NMR spectrum.
Residual solvent and water peaks were then removed by cutting, and the spectra were normalized using the Total Area method to a total area of 100 units. The data was then exported for further analyses.
Cosine similarity scores network: We employed a cosine similarity scores analysis to determine if samples that are closely related to one another on the Radula phylogenetic tree were also more similar chemically. A cosine similarity scores network was created from
1H-NMR data of all the samples to evaluate intra- and interspecies metabolome similarities/differences. As discussed in Chapter 1, this workflow involves converting 1H-
NMR peak area data for each sample into a unit vector in order to calculate cosine similarity scores pairwise for all samples. A score of 0 between two samples would indicate those samples share no similarities in their 1H-NMR data, whereas a score of 1 between two samples would indicate identical samples. The cosine similarity score cutoff was set to 0.7 after manual inspection of the dataset. Pairwise correlations with cosine similarity scores less than the 0.7 cutoff were removed from the network. The network was further filtered to only include sample connections (edges) between samples whose cosine similarity 21 scores appeared in the top 10 scores for the other sample.33 For example, if sample X contained sample Y in its top 10 scores, then sample Y must also contain sample X in its top 10 scores for this edge to be retained in the network. Nodes in this network are whole plant metabolomes, and edges connecting the nodes represent plant metabolomes that share chemical features in their 1H-NMR data and therefore have more similar plant metabolomes. The network was visualized using Cytoscape.34
Weighted Gene Co-Expression Network Analysis (WGCNA): Where a cosine similarity scores analysis reveals if whole plant metabolomes are similar to one another,
WGCNA adds another layer of resolution indicating which 1H-NMR peaks may be at the root of the observed similarity between different samples. We applied WGCNA to identify clusters of co-varying 1H-NMR peaks which are grouped together into various modules.
We hypothesized that 1H-NMR peaks that fall into the same module and therefore co-vary with each other are likely from similar structures or may be part of different compounds that often occur in phytochemical mixtures together. When modules are conserved across the Radula phylogeny, this could be indicative of certain biosynthetic pathways being employed in distantly related species. Through annotation of modules of interest, we can begin to elucidate patterns in the evolution and conservation of phytochemical diversity in the Radula clade.
2.3 Results
Cosine similarity scores network: The cosine similarity scores analysis revealed three clusters of nodes (Figure 2.3.1). Gray nodes represent plant species with only one representative individual. Colored nodes represent plant species with more than one representative sample which may have been collected from different collections. Three 22 main clusters were observed (A, B, and C in Figure 2.3.1). We first searched for patterns of individuals of the same species clustering together in the network, which would suggest some components of the plant’s phytochemical mixtures are conserved in the species even if samples are collected from different locations. We observed samples of P. culebranum
(green), for example, were more similar to each other than other plant samples. On the other hand, two P. colonense (orange) individuals group into separate clusters from each other, suggesting these individuals share more chemical features with more distantly related species than they do with each other. Despite including over 50 different species in this study, we still see samples that are highly correlated to one another indicating there is overlap in their phytochemical mixtures.
C
A
B P. polytrichum P. colonense P. crassinervium P. culebranum P. cyanophyllum P. hispidum P. disparipes P. lucigaudens Species with only 1 representative sample
Figure 2.3.1. Cosine similarity scores network of 1H-NMR data from all Radula samples. Gray nodes represent species with only 1 representative sample in the dataset. Colored nodes represent species represented by multiple samples from different locations. Three main clusters exist in the network, labeled A, B, and C. 23
WGCNA: A hierarchical cluster analysis (HCA) identified 30 modules of co-varying 1H-
NMR chemical shifts. Module eigengenes (MEs) were determined for each module
(Chapter 1) and a dendrogram created from module MEs was used to assess closely related modules (Figure 2.3.2). We also visualized the network of 1H-NMR data where nodes are representative of 1H-NMR chemical shift values and are colored by their module membership (Figure 2.3.3). Many modules contained chemical shift values in the less crowded, downfield region of the 1H-NMR spectrum which can facilitate assignment of structural features to these modules. Therefore, we began by annotating 5 modules (cyan, yellow, green, blue, darkorange, and grey60) looking for characteristic chemical shift values for a variety of classes of secondary metabolites contained within these modules.
Manual inspection of 1H-NMR spectra of samples with high ME values for each module resulted in the following assignments of secondary metabolite classes: cyan – flavonoids, lignans, alkenyl phenols; yellow – alkaloids; green – prenylated benzoic acids, flavonoids; blue – flavonoids, hydroxylated methylenes; darkorange – prenylated compounds; grey60
– prenylated compounds (Figure 2.3.3).
24
A EigenvalueEigenvalue dendrogramDendrogram
1.0
0.8
MElightcyan MEorange MEdarkorange MEblue
0.6 MEwhite MEgreenyellow MEdarkgreen MEskyblue MEpurple MElightyellow MEturquoise 0.4
MEgreen MEtan MEgrey60 MEblack MEcyan MEmagenta MEyellow MEdarkgrey MEroyalblue MEpink MEred MEdarkturquoise 0.2 MEmidnightblue MEbrown MEdarkred MEsalmon MEsteelblue
MElightgreen MEsaddlebrown Eigenvalue Adjacency Heatmap B Eigenvalue adjacency heatmap
1
0.8
0.6
0.4
0.2
0
Figure 2.3.2. A) Eigenvalue dendrogram showing related modules. B) Eigenvalue adjacency heatmap. Rows and columns are modules, revealing modules that are either positively or negatively correlated with one another.
25
Flavonoids Lignans * * Alkenyl phenols Prenylated benzoic acids * O O * * * O O O
* O * * HO * OH * O
O OH * * O * * Flavonoids
O
Alkaloids * O O * N
* O
* O
Flavonoids * * * O * * * * * O OH Hydroxylated methylenes
Prenylated compounds *
Prenylated compounds
*
Figure 2.3.4. WGCNA network. Nodes are individual chemical shifts and are colored by their module membership.
26
Patterns in modules mapped onto Radula phylogeny: To look for patterns of module representation across the Radula phylogeny, our collaborator Katie Uckele (UNR – Dept. of Biology) constructed the Radula phylogeny, and module ME values from the WGCNA were mapped onto the tree topology. The data was tested for phylogenetic signal (Uckele et al. in prep) where high phylogenetic signal for a module indicates the chemical information contained in that module can be predicted based off the phylogeny, while low phylogenetic signal would indicate the evolution of the structural information contained in the chemical dataset is labile.
The cyan, darkorange, and grey60 modules had high phylogenetic signal, indicating the presence of these modules can be predicted from the Radula phylogeny. As discussed above, the cyan modules contains chemical shift data consistent with flavonoids, lignans, and alkenyl phenols while the darkorange and grey60 modules contain chemical shift data consistent with prenylated compounds. Therefore, the results suggest these structural features are conserved across the Radula phylogeny. On the other hand, the yellow, blue, and green modules had low phylogenetic signal. The yellow module was consistent with alkaloid features, the blue module was consistent with flavonoids and hydroxylated methylenes, and the green module was consistent with prenylated benzoic acids, and flavonoids. Because these modules displayed low phylogenetic signal, we hypothesize that the structural features contained in these modules are labile (Figure 2.3.4). 27
Prenylated Prenylated benzoic acids Alkaloids Compounds* Flavonoids
Prenylated Flavonoids Flavonoids Compounds* Hydroxylated methylenes Lignans Alkenyl phenols* Figure 2.3.4. Distribution of module representation across the Radula phylogeny for a subset of annotated modules (darkorange, grey60, green, blue, yellow, and cyan). The darkorange, grey60, and cyan modules had high phylogenetic signal(*) while the green, blue, and yellow modules were more randomly distributed across the phylogeny. 28
2.4 Discussion
This work aims to understand the evolution of phytochemical diversity in the tropical plant genus Piper. Our first goal was to determine if closely related species on the
Radula phylogenetic tree produce more similar phytochemical mixtures compared to more distantly related species. A cosine similarity scores network analysis compared the phytochemical mixtures across all the samples (Figure 2.3.1). If sister species were to produce more similar phytochemical mixtures to one another, we would expect to see clusters of samples arise in the network that correspond to more closely related species on the phylogenetic tree. This pattern would support the co-evolutionary arms race hypothesis.2 However, our analysis revealed only three clusters in the network. This was an important observation as it suggests that, despite the inclusion of over 50 different species in this study, there are still enough similarities across their phytochemical mixtures to cause distantly related samples to be chemically related to one another. This observation can be explained by the nature of 1H-NMR data, where similar yet distinct structural features are related enough to result in seemingly similar phytochemical mixtures.
Therefore, rather than entire phytochemical mixtures being conserved/predictable, it is likely that only certain structural features and their corresponding biosynthetic pathways are conserved across the phylogeny.
WGCNA provided an additional layer of resolution to the chemical dataset by identifying groups of 1H-NMR chemical shift values that may be conserved across the
Radula phylogeny. We began by focusing on a subset of modules (cyan, yellow, green, blue, darkorange, and grey60) that contained chemical shift data in the less crowded, downfield region of the 1H-NMR spectrum and were able to assign certain structural 29 features from a variety of classes of secondary metabolites to various modules (Figure
2.3.3). Some classes of compounds, like flavonoids and prenylated compounds, had different parts of their core structure represented by different modules. Structural features corresponding to the same compound class in two modules suggests the chemical information contained in these modules results from the same biosynthetic pathway. The cyan, darkorange, and grey60 modules all exhibited high phylogenetic signal, indicating the structural features represented by these modules are not very labile and are instead conserved across the phylogeny. The darkorange and grey60 modules both contain chemical shift data consistent with different parts of prenylated compounds, supporting the idea that two modules that have chemical shift data related to the same compound class are constructed from the same biosynthetic pathways.
Phylogenetic signal for both the prenyl modules (darkorange and grey60) was of interest to us. Prenyl groups are classified as terpenes/terpenoids, which are evolutionarily one of the oldest, largest, and most diverse classes of secondary metabolites.35 Therefore, if any class of secondary metabolite should have phylogenetic signal, we might expect terpenes/terpenoids to have strong phylogenetic signal. This finding is consistent with the screening hypothesis, which posits that plants maintain high phytochemical diversity to increase the likelihood that a plant will produce a potent compound or precursor, such as a terpene/terpenoid, that will go on to mediate interactions with the plant’s environment.
Furthermore, because these modules exhibit high phylogenetic signal, this would indicate that these conserved structural features are more biologically relevant for the samples with high ME values for these modules compared to more distantly related samples on the
Radula phylogeny. 30
WGCNA also revealed some modules (cyan, green, and blue) contained chemical shift data consistent with multiple compound classes. The co-variance of the structural features from different compound classes across the samples included in this study suggests these compounds are often found together in various phytochemical mixtures. This pattern of co-occurrence of structural features across different plant species would suggest these compounds originate from the same biosynthetic pathways that branched off at various points to form different, but related products. Furthermore, this finding also supports the screening hypothesis because maintaining high phytochemical diversity through the production of multiple compound types further increases a plant’s likelihood of producing a biologically important compound.
2.5 Conclusions and future directions
This work demonstrates the utility of a non-targeted metabolomic approach in evaluating hypotheses related to the origins of secondary metabolite diversity, including the co-evolutionary arms race and screening hypotheses. Our aims were to: 1) Compare phytochemical mixtures of sister species and distantly related species, 2) Identify groups of chemical shifts values (modules) that may be conserved or randomly distributed across the Radula phylogeny, and 3) Assign structural features to modules of chemical shift values to elucidate trends in the evolution of phytochemical diversity and biosynthetic pathways.
A cosine similarity scores analysis revealed that there is overlap in phytochemical mixtures of distantly related species and that sister species are not necessarily more chemically related to one another. WGCNA was used to identify modules of 1H-NMR chemical shift values, and through our collaboration, we were able to determine which modules exhibit high phytochemical signal and those that are randomly distributed across the phylogeny. 31
We were able to assign structural features to a number of modules and determined the terpene/terpenoid biosynthetic pathways may be phylogenetically conserved. Taken together, our results provide strong support for the screening hypothesis where plants maintain high phytochemical diversity, potentially through branching biosynthetic pathways, to increase their odds of producing a product or precursor critical to mediating the plant’s interactions with its environment. This study highlights the power of relying on patterns in mixtures rather than structure elucidation for individual molecules in making phytochemical comparisons at unprecedented taxonomic and geographic scales.
Future work on this project includes continuing to annotate the remaining modules from this dataset in addition to collecting liquid chromatography – mass spectrometry (LC-
MS) data for each sample. Metabolomics with LC-MS data will add another layer of resolution to the dataset and allow us to more confidently assign structural features to modules from the preliminary 1H-NMR networks. We will use the annotated modules to help better understand phylogenetic trends in phytochemical diversity and biosynthesis, enabling us to continue addressing long-standing ecological questions and hypotheses. The consequences of phytochemical diversity for various herbivores are explored in the following chapters.
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Phytochem. Rev. 2016, 15, 1153-1166. 35
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C.S. Phylogenetic signal and the evolution of secondary chemistry in Radula (Piperaceae): persistent trait correlations despite rampant trait liability. In preparation.
36
Chapter 3: Small Mammal Metabolism Across a Sharp Environmental Gradient
3.1 Introduction
Diets rich in plant secondary metabolites are known to mediate complex and specific interactions between organisms.1 Investigating plant-mammal interactions can further contribute to our understanding of the dietary role in ecological adaptation. The relationship between ecological adaptation and separation between related species remains a strong topic of interest in evolutionary biology and chemical ecology. Therefore, sharp habitat transitions between two closely related species are appropriate environments for investigating diet’s role in ecological adaptation.
In collaboration with Dr. Marjorie Matocq (UNR-Biology/EECB), Danny Nielsen
(UNR-Biology/EECB), and Dr. Lora Richards (UNR-Biology/EECB), we have been investigating diet’s role in the separation of two woodrat (Neotoma) species, N. bryanti and
N. lepida. N. bryanti and N. lepida are two geographically distinct species whose habitats converge at a sharp ecological transition area in Kelso Valley, CA, creating a hybrid zone.2,3 N. bryanti lives in a moist, mesic habitat herein referred to as the “hill” while N. lepida lives in a dry, xeric habitat referred to as the “flats.” The presence of the hybrid zone provides the opportunity for these two species to interbreed (Figure 3.1.1) (Matocq et al. in review).3 However, many hybrid offspring of N. bryanti and N. lepida do not survive into adulthood.3 Because Neotoma species in general are known to consume habitat specific diets4 high in different toxins and with relatively low nutritional value,2,5-9 it has been hypothesized that hybrids are unable to make a successful transition to one of the two 37 habitat-specific adult diets.3 Therefore, diet is thought to be a substantial driving force in the habitat separation of N. bryanti and N. lepida.
N. lepida “flats” N. bryanti “hill”
Figure 3.1.1. N. bryanti and N. lepida distribution across the hybrid zone.
The diets of N. bryanti and N. lepida are dominated by a single plant genus in each of the two habitats (Figure 3.1.2) (Matocq et al. in review). Rhamnus makes up 52% of N. bryanti’s diet in the hill and contains anthraquinones,10 terpenoids, alkaloids, flavonoids,11 anthocyanins,12 and cascarosides.13 Prunus makes up 48% of N. lepida’s diet in the flats and contains cyanogenic glycosides,14 flavonoids,15 anthocyanins,16 steroids/terpenoids,17 coumarins,18 and gibberellins19 (Table 3.1.1).20
38
flats hill
Prunus Rhamnus
Rhamnus Prunus
Figure 3.1.2. Main diet components in the flats and the hill.
Table 3.1.1. Compound Classes Identified in Rhamnus and Prunus
Genus Compound Class Example Compound Reference Rhamnus anthraquinones emodin 11
terpenoids thymol 11
alkaloids isoboldine 11
flavonoids kaempferol 11
anthocyanins delphinidin 3-O-rutinoside 12
cascarosides cascaroside A 13
Prunus cyanogenic glycosides amygdalin 14
flavonoids apigenin 15
anthocyanins cyanidin 16
steroids/terpenoids alphitolic acid 17
coumarins coumarin 18
gibberellins gibberellin A1 19 39
Although the plant chemistry of the major components in each diet has been explored, little is known about the minor plant components in either diet, which may play a critical role in the observed habitat separation or may be synergistic with the major plant components.21 Nevertheless, other plants and their toxic effects on various species of
Neotoma have been well-characterized.5,22-27 For example, juniper (Juniperus spp.) produces monoterpenes such as α-pinene,9,28 which is known to cause central nervous system depression, liver and kidney damage, and occasionally death in mammals.29-31
Despite the observed toxicity of α-pinene, the specialist herbivore, N. stephensi, can successfully consume and detoxify α-pinene in addition to other monoterpenes found in juniper plants32-34 while generalist N. albigula does so with decreased efficiency. Mounting evidence suggests that N. stephensi’s ability to consume these toxic compounds is likely controlled by liver biotransformation enzymes35,36 and an enhanced ability to minimize absorption of toxic compounds33 which may be due in part to the microbiome.37
The relationship between Neotoma generalists and specialists has been well-studied with the goal of investigating the basis of varying abilities to consume different diets.5,9,22,25,36,38-42 It has been hypothesized that specialists tend to utilize phase I enzymes, also known as functionalization enzymes, to reduce the toxicity of ingested toxins over phase II clearing enzymes.43,44 Examples of phase I enzyme transformations include hydrolysis, reduction, and oxidation by liver microsomal P450 enzymes.43,45-48 After a toxin has been functionalized in phase I, it can either be excreted by the mammal or it may undergo phase II metabolism. Phase II enzymes are also known as conjugation enzymes because they couple conjugates such as glucuronides,49,50 sulfates,51-53 glutathiones,54,55 amino acids, acetyl, or methyl groups to the already functionalized compound.43,56 These 40 processes increase the water-solubility of a toxin or minimize its toxicity to aid in its excretion34 (Figure 3.1.3).
R S R R O O O R O H N O Glutathione HO N OH Oxidation H conjugation NH2 O
R Phase I Phase II O R X S OH X = O, S, N Sulfation Oxidation O Hydrolysis R SH O Reduction R OH R NH2 Acetylation R
OH O HO O Glucuronidation HO R OH
Lipophilic Hydrophilic Figure 3.1.3. Phase I and phase II transformations.
We predicted that the various metabolic transformations utilized by mammals comparable to Neotoma would result in unique chemical profiles best studied using a non- targeted metabolomic approach. A non-targeted approach is especially appropriate when considering enzymatic transformations of compounds and changes in endogenous chemistry in which a specific compound or compound class may have undergone significant structural changes. We expected that toxins that have similar modes of action would result in similar Neotoma metabolomes, and in cases where the toxin of interest isn’t discernible, changes in endogenous chemistry in response to the toxin would still result in unique metabolic fingerprints. This study’s focus is on fecal matter as it is easier to collect in a field study than other biofluids such as urine or blood. Using a non-targeted 41 metabolomic approach, we wanted to test the hypotheses that: 1) N. bryanti and N. lepida will have unique metabolic responses to diets high in different toxins, and 2) Metabolic fingerprints of N. bryanti and N. lepida will be distinct even when they consume the same plants.
3.2 Methods
Sample collection and diet determination: Fifteen fecal samples were collected from both the hill and the flats to reach a total of 30 samples. Samples were transported in paper envelopes to UNR for analysis. A portion of each fecal samples was analyzed for plant
DNA content according to Matocq et al. (in review) to yield proportions of plant components in each individual’s diet. The remaining portion of each fecal sample was used for metabolomic studies.
Fecal metabolomics: All fecal samples were extracted using an optimized protocol adapted from Santos Pimenta et al.57 The method described by Santos Pimenta et al.57 is a streamlined approach for quantifying the cyanogenic glycosides in Prunus, requiring no additional purification steps. Because Prunus is the main diet component for N. lepida living in the flats, this method served as a starting point for developing an optimized extraction method that would be applicable to many of the diets and feces from individuals in either habitat.
Approximately 200 mg of each fecal sample was frozen in liquid nitrogen and ground to a fine powder using mortar and pestle. Samples were then transferred to pre- tared screwtop vials and combined with 2 mL of a mixture of 50% KH2PO4 buffer (90 mM, pH = 6.0) in D2O with 0.01% TMSP-d4 and 50% CD3OD. Each sample was vortexed for
1 minute, sonicated for 15 minutes, and centrifuged for 15 minutes. Following 42 centrifugation, 0.5 mL of each sample was filtered into a NMR tube using a 0.45 μm PTFE membrane filter. The remaining liquid extract was filtered into a pre-tared 2-dram vial and dried under nitrogen with reduced pressure to remove methanol. The remaining aqueous suspension was lyophilized and samples were stored in a freezer for further analysis.
Samples were analyzed using 1H-NMR spectroscopy and LC-MS. 1H-NMR spectra were collected on a Varian 400 MHz NMR spectrometer. MestReNova software
(Mestrelab Research, Spain) was used to process the NMR data. LC-MS data were collected using an Agilent 6200 time-of-flight (TOF) LC-MS with an electrospray
57 ionization (ESI) source. Santos Pimenta et al. used TMSP-d4 in their buffered extraction protocol as an internal standard for their 1H-NMR analysis. However, we were concerned about a potentially large salt peak appearing in the LC-MS chromatograms because of the
TMSP that was used for the extractions. Therefore, to prepare samples for LC-MS analysis,
NMR samples were joined with their dried fecal extracts and combined with 3 mL H2O with 0.1% formic acid. The aqueous suspension was lyophilized and a new extract mass was calculated. To bring all samples to a concentration of approximately 1 mg/mL, an appropriate volume of methanol was added to each sample, and a 20 μL aliquot was transferred to a LC-MS vial and diluted with 180 μL of water. MZmine 258 was used to process LC-MS data and produce an aligned peak list. The aligned peak list was used to create the molecular networks discussed in the following sections. Networks were created and visualized using Cytoscape.59
3.3 Results
Plant diet determination: DNA analysis of feces from each individual revealed which plants were being consumed by individuals and in what proportion, however we chose to 43 focus only on the major diet components in each habitat (Rhamnus and Prunus) (Matocq et al. in review) (Table 3.3.1). As expected, most individuals from the hill (N. bryanti) were consuming plants from the Rhamnus genus, and most individuals from the flats (N. lepida) were consuming plants from the Prunus genus. Unexpectedly, one individual from the hill
(H14) was consuming Prunus from the flats, and two individuals from the flats (F5 and
F7) were consuming Rhamnus from the hill. None of the individuals studied were consuming both Rhamnus and Prunus plants.
Table 3.3.1. Proportion of Rhamnus or Prunus Plant DNA Found in Each Individual’s Feces
Individuala Rhamnus Prunus F1 0 0.768297 F2b 0 0 F3b 0 0 F4 0 0.202563 F5 0.290522 0 F6 0 0.442892 F7 0.128576 0 F8 0 0.995781 F9 0 0.703652 F10 0 0.889097 F11 0 0.82627 F12 0 0.79958 F13 0 0.542626 F14 0 0.834827 F15 0 0.289903 H1 0.651101 0 H2 0.566471 0 H3 0.567521 0 H4 0.96952 0 H5 0.686868 0 H6 0.673626 0 H8 0.834386 0 H9 0.680517 0 H10 0.195247 0 H11b 0 0 44
H13b 0 0 H14 0 0.366269 H15 0.967356 0 a F# indicates individual from the flats (N. lepida). H# indicates individuals from the hill (N. bryanti). There is no plant DNA in feces data for individuals H7 and H12. b These individuals had neither Rhamnus nor Prunus plant DNA in their feces.
Fecal metabolomics: As mentioned in the Methods section (3.2), we only started with approximately 200 mg of each fecal sample for the extractions. These small starting masses yielded low extract masses and therefore dilute NMR samples. After concentrating the samples, NMR samples still proved to be very dilute and it was difficult to distinguish smaller peaks from baseline noise in the spectra. Furthermore, although structurally unique, many of the compounds in Table 3.1.1 can have potentially overlapping signals in 1H-NMR spectra, especially if many of these compounds appear in the same mixture. Additionally, given the low concentrations of samples being analyzed, we risked overlooking minor components of the fecal metabolome that may be critical to our analyses. Therefore, we decided to pursue a molecular networking approach to visualize relationships between woodrats and their diet using only data from LC-MS analysis, which offers greater sensitivity and has the added advantage of separating components of a mixture before their masses are analyzed.
Network analysis: Two networks were created from the feces LC-MS data (Figure 3.3.1 and Figure 3.3.2). Both networks have three sets of nodes. The top tier of nodes represents the major diet components in either habitat. These nodes are connected by weighted edges to N. bryanti individuals in the hill or N. lepida individuals in the flats (second tier of nodes) who were found to be eating these plants through DNA analysis. Edges are weighted by the amount of plant DNA identified in the individual’s feces (Table 3.3.1), with thicker 45 lines indicating a greater proportion of that plant DNA was identified in the feces.
Individuals in the hill and the flats are connected by unweighted edges to LC-MS features identified in their feces. The networks in Figure 3.3.1 and Figure 3.3.2 differ only in the woodrat individuals included in the analysis.
Rhamnus Prunus Rhamnus Prunus
46
Rhamnus Prunus
H1 H2 H3 H4 H5 H6 H8 H9 H10 H15 F5 F7 F1 F4 F6 F8 F9 F10 F11 F12 F13 F14 F15 H14
Rhamnus Prunus H1 H2 H3 H4 H5 H6 H8 H9 H10 H15 F5 F7 F1 F4 F6 F8 F9 F10 F11 F12 F13 F14 F15 H14
305.266 440.413 636.254 305.266 440.413 636.254 328.256 154.124 294.247 172.134 328.256 154.124 294.247 H1 H2 H3 H4 H5 172.134 H6 H8 H9 H10 H15 F5 F7 F1 F4 F6 F8 F9 F10 F11 F12 F13 F14 F15 H14 310.241 174.150 279.209 309.146 310.241 174.150 279.209 309.146 267.161 278.250 160.134 267.161 278.250 160.134 skyblue 249.198 251.214 orange 100.113 267.208 312.259 H1 H2 H3 H4 H5 H6 H8 H9 H10 H15 F5 F7 F1 F4 F6 F8 F9 F10 F11 F12 F13 F14 F15 H14 249.198 251.214 deep pink gold 158.118 225.197 314.278 100.113 267.208 312.259 1198.594 295.154 202.049 288.263 338.344 222.021 255.161
1184.615 415.323 439.360 coral 463.309 758.474 445.298 158.118 225.197 314.278 305.266 440.413 636.254 449.329 315.124 563.555 158.155 1198.594 295.154 202.049 416.328 315.125 463.308 light green 305.266 440.413 636.254 288.263 479.303 312.262 312.263 338.344 222.021 255.161 aquamarine 258.112 300.299 183.103 328.256 154.124 294.247 279.234 433.334 431.319 172.134 328.256 154.124 294.247 235.218 280.265 365.139 149.025 172.134 304.258 281.188 417.339 magenta 1184.615 415.323 439.360 284.297 431.318 320.259 463.309 758.474 445.298 197.119 353.081 144.103 447.314 417.338 356.329 314.273 310.241 174.150 279.209 296.261 296.260 136.062 461.294 265.193 309.146 365.138 381.112 337.107 235.217 162.113 399.254 184.065 449.329 315.124 563.555 310.241 174.150 279.209 140.144 410.330 298.277 309.146 158.155144.066 197.118 105.036 116.019 263.239 267.161 278.250 160.134 416.328 315.125 463.308 253.218 118.087 282.281 104.108 light pink turquoise 267.161 278.250 160.134 cyan 447.313 479.303 312.262 312.263 Rhamnus diet 249.198Hill rats Rhamnus251.214 & Prunus metabolites gray 258.112 300.299 183.103 279.234 433.334 431.319 235.218 280.265 365.139 149.025 100.113 Prunus diet Hill rats Rhamnus metabolites & Flat rats Prunus metabolites 304.258 281.188 417.339 267.208 312.259 284.297 431.318 320.259 197.119 353.081 144.103 447.314 Hill rats eating Rhamnus Ubiquitous metabolites 417.338 356.329 314.273 249.198 251.214 296.261 296.260 136.062 461.294 265.193 365.138 381.112 337.107 235.217 Flat rats eating Rhamnus Hill rat Rhamnus & Prunus metabolites & Flat rats Prunus metabolites 162.113 399.254 184.065 158.118 225.197 314.278 140.144 410.330 298.277 100.113 144.066 197.118 105.036 116.019 263.239 Flat rats eating Prunus Hill rats Rhamnus metabolites267.208 & Flat rats Rhamnus312.259 & Prunus metabolites 1198.594 295.154 202.049 253.218 118.087 282.281 104.108 Hill rats eating Prunus Flat rats Rhamnus & Prunus metabolites 288.263 338.344 222.021 255.161 Hill rats Rhamnus metabolites Flat rats Prunus metabolites 447.313 1184.615 415.323 439.360 Hill & Flat rats Rhamnus metabolites Hill & Flat rats Prunus metabolites 463.309 758.474 445.298 158.118 225.197 314.278
Hill & Flat rats Rhamnus metabolites Hill rat Prunus metabolites 449.329 315.124 563.555 & Hill rat Prunus metabolites 158.155 1198.594 295.154 202.049 416.328 315.125 463.308 288.263 479.303 312.262 312.263 338.344 222.021 255.161 Figure 3.3.1. Tripartite network relating plant diet to Neotoma individuals consuming258.112 300.299 183.103 279.234 433.334 431.319 those plants and Neotoma individuals to their metabolites. 235.218 280.265 365.139 149.025 304.258 281.188 417.339 1184.615 415.323 439.360 284.297 431.318 320.259 463.309 758.474 445.298 197.119 353.081 144.103 447.314 417.338 356.329 314.273 296.261 296.260 136.062 461.294 265.193 365.138 381.112 337.107 235.217 162.113 399.254 184.065 449.329 315.124 563.555 140.144 410.330 298.277 158.155144.066 197.118 105.036 116.019 263.239 416.328 315.125 463.308 253.218 118.087 282.281 104.108
447.313 479.303 312.262 312.263 Rhamnus diet Hill rats Rhamnus & Prunus metabolites 258.112 300.299 183.103 279.234 433.334 431.319 235.218 280.265 365.139 149.025 Prunus diet Hill rats Rhamnus metabolites & Flat rats Prunus metabolites 304.258 281.188 417.339 284.297 431.318 320.259 197.119 353.081 144.103 447.314 Hill rats eating Rhamnus Ubiquitous metabolites 417.338 356.329 314.273 296.261 296.260 136.062 461.294 265.193 365.138 381.112 337.107 235.217 Flat rats eating Rhamnus Hill rat Rhamnus & Prunus metabolites & Flat rats Prunus metabolites 162.113 399.254 184.065 140.144 410.330 298.277 144.066 197.118 105.036 116.019 Flat rats eating Prunus Hill rats Rhamnus metabolites & Flat rats Rhamnus & Prunus metabolites 263.239 253.218 118.087 282.281 104.108 Hill rats eating Prunus Flat rats Rhamnus & Prunus metabolites Hill rats Rhamnus metabolites Flat rats Prunus metabolites 447.313 Hill & Flat rats Rhamnus metabolites Hill & Flat rats Prunus metabolites Hill & Flat rats Rhamnus metabolites Hill rat Prunus metabolites & Hill rat Prunus metabolites 47
Figure 3.3.1 contains all individuals for which plant DNA in the feces indicated they were consuming Rhamnus or Prunus diets. Ten individuals from the hill and two individuals from the flats were consuming Rhamnus. Eleven individuals from the flats and one individual from the hill were consuming Prunus. It’s important to note that none of the individuals included in this study were consuming only Rhamnus or Prunus as other diet components were also identified in the feces. Therefore, it is likely that not all metabolites seen in the network are the result of metabolism of compounds present in Rhamnus or
Prunus. This network analysis (Figure 3.3.1) resulted in 12 distinct clusters of m/z features, where m/z features are defined as m/z and retention time pairs. Importantly, not every individual from a habitat needed to produce a m/z feature for the feature to be included in a cluster. Moving from left to right in the network, the gold, coral, aquamarine, light pink, turquoise, and deep pink clusters did not reveal any obvious patterns in similarities or differences in metabolism between N. bryanti and N. lepida individuals. However, the orange cluster represents m/z features from some N. bryanti individuals consuming
Rhamnus, which could potentially indicate these features are the result of Rhamnus metabolism. The cyan cluster contains m/z features from N. bryanti individuals eating
Rhamnus and N. lepida individuals eating Prunus. Therefore, this cluster could represent metabolites produced by woodrats eating their main diet component where their system is not under any kind of metabolic stress from consuming a toxic plant they are not accustomed to eating. Alternatively, this cluster could be the result of shared minor diet components between the two woodrat species. The gray cluster represents ubiquitous metabolites produced by N. bryanti and N. lepida individuals regardless of the main diet component. This cluster may result from general metabolism not related to a specific diet 48 component. The magenta cluster contains m/z features from N. lepida individuals eating
Rhamnus and N. lepida individuals eating Prunus. These features could be endogenous metabolites commonly produced by N. lepida individuals independent of their diet. The light green cluster shows m/z features from N. lepida individuals eating Prunus, indicating these metabolites could be directly related to the metabolism of the toxic compounds present in Prunus plants. Lastly, the skyblue cluster represents m/z features produced by the only N. bryanti individual consuming Prunus. This could indicate that the N. bryanti individual is processing Prunus differently than the N. lepida individuals because the N. bryanti individual is not as metabolically efficient at detoxifying the cyanogenic glycosides in Prunus as the N. lepida individuals are.
The inclusion of the N. lepida rats eating Rhamnus and the N. bryanti rat eating
Prunus complicated the analysis described above (Figure 3.3.1). Because one of our main goals was to determine if N. bryanti and N. lepida produce different metabolites as a result of their distinct diets (Matocq et al. in review), we pursued a simpler network analysis of only N. lepida eating Prunus and N. bryanti eating Rhamnus (Figure 3.3.2; Table 3.6.1;
Table 3.6.2; Table 3.6.3). Ten individuals from the hill were consuming Rhamnus, and 11 individuals from the flats were consuming Prunus.
Three unique clusters resulted: one for N. bryanti consuming Rhamnus, one for metabolites produced by both N. bryanti and N. lepida, and one for N. lepida consuming
Prunus. The ubiquitous m/z features could be the result of other shared diet components or endogenous metabolism. Despite overlap in their diets, these woodrat species are producing unique sets of metabolites that could relate to both metabolism of their major 49
diet components as well as different methods of metabolizing the same minor plant
components.
R P
H1 H2 H3 H4 H5 H6 H8 H9 H10 H15 F1 F4 F6 F8 F9 F10 F11 F12 F13 F14 F15
U42 U47 U22 U18 U39 U49 U29 U3 U1 U35 U45 U41 U23 U25 B22 B4 B6 B16 B1 B2 A9 A7 A2 A11 A28 U58 U32 U37 U13 U40 U5 U51 B35 B32 B21 B23 B7 B38 A6 A8 A13 A4 A21 U9 U26 U55 U62 U14 U48 U31 B13 B26 B29 B17 B20 B24 A27 A1 A19 A18 A20 U4 U56 U8 U43 U44 U52 U57 B11 B33 B8 B18 B37 B40 A24 A26 A16 A10 A22 U28 U33 U6 U15 U11 U24 U12 B10 B36 B30 B9 B14 B31 A15 A5 A25 A3 A29 U34 U20 U27 U46 U2 U54 U10 B25 B15 B39 B34 B3 B27 A30 A23 A14 A12 A17 U17 U53 U38 U61 U50 U60 U19 B5 B28 B19 B12 U59 U30 U16 U7 U21 U36 Rhamnus diet Prunus diet Hill rats eating Rhamnus Flats rats eating Prunus Hill rats Rhamnus diet metabolites Flats rats Prunus diet metabolites Ubiquitous metabolites R P Figure 3.3.2. Simplified tripartite network relating plant diet to Neotoma individuals H1 H2 H3 H4 H5 H6 H8 H9 H10 H15 F1 F4 F6 F8 F9 F10 F11 F12 F13 F14 F15 consuming those plants and Neotoma individuals to their metabolites.
U42 U47 U22 U18 U39 U49 U29 U3 U1 U35 U45 U41 U23 U25 B22 B4 B6 B16 B1 B2 A9 A7 A2 A11 A28 U58 U32 U37 U13 U40 U5 U51 B35 B32 B21 B23 B7 B38 A6 A8 A13 A4 A21 3.4 Discussion U9 U26 U55 U62 U14 U48 U31 B13 B26 B29 B17 B20 B24 A27 A1 A19 A18 A20 U4 U56 U8 U43 U44 U52 U57 B11 B33 B8 B18 B37 B40 A24 A26 A16 A10 A22 U28 U33 U6 U15 U11 U24 U12 B10 B36 B30 B9 B14 B31 A15 A5 A25 A3 A29 U34 U20 U27 U46 U2 U54 U10 B25 B15 B39 B34 B3 B27 A30 A23 A14 A12 A17 We were interested in whether or not N. bryanti and N. lepida individuals U17 U53 U38 U61 U50 U60 U19 B5 B28 B19 B12 U59 U30 U16 U7 U21 U36 consuming the major diet component in the adjacent habitat would excrete toxins or
produce different metabolites compared to individuals experienced with the diet. Fecal
matter was used in this study due to the relative ease of collection in the field. Additionally,
fecal matter can contain unmetabolized toxins that would not be found in urine or blood
samples. In the network shown in Figure 3.3.1, there was not a cluster of m/z features
corresponding only to the N. lepida rats eating Rhamnus. However, as mentioned above,
there was a small cluster (skyblue) of m/z features for the N. bryanti individual consuming
Prunus, indicating there may be a metabolic difference between the N. lepida and N. 50 bryanti individuals. Therefore, we chose to pursue this cluster by looking more closely at the metabolism of amygdalin, a cyanogenic glycoside found in Prunus, and how it could differ between experienced N. lepida rats and the inexperienced N. bryanti rat eating
Prunus.
Prunus plants are known to produce both amygdalin and prunasin.60 Although these compounds are both cyanogenic glycosides, they are not toxic to the plant because of the attached sugars. When plant tissue is disrupted by an herbivore feeding on the plant, the sugars are cleaved from amygdalin and prunasin in order to release toxic hydrogen cyanide
(HCN). Any remaining HCN that is not consumed by the herbivore is further detoxified by the Prunus plants through the action of β-cyanoalanine synthase (Figure 3.4.1).61-63
51
O
OH NH 2 Amygdalin Biosynthesis phenylalanine Cyanogenesis P450 HCN Detoxification
N HO phenylacetaldoxime
P450
OH O O CN NH HCN CN 3 H HS OH
NH2 O mandelonitrile Glc NHbenzaldehyde3 HCN cysteine prunasin β-cyanoalanine synthase
H2S UDP-glucosyltransferase β-glucosidase O
H2S HO CN NH2 CN β-cyanoalanine O Glc O NIT4 nitrilase Glc O O
amygdalin H2N HO OH O NH3 HCN
O NH2 O NH3 asparagine aspartate
Figure 3.4.1. Amygdalin biosynthesis, cyanogenesis, and cyanide H2S detoxification in Prunus and other cyanogenic plants.
Enzymatic β-glucosidase is known to be involved in the hydrolysis of amygdalin to prunasin in mammals.64 Therefore, woodrats may be able to minimize the toxic effects of HCN by inhibiting sugar cleavage through downregulation of β-glucosidase and/or upregulation of UDP-glucosyltransferase. We hypothesized that N. lepida may be regulating these enzymes more efficiently than N. bryanti, thereby ensuring the sugar moieties remain attached and HCN toxicity is avoided. 52
The β-cyanoalanine synthase enzyme identified in plants is not known in mammals.
Instead, mammals rely on the rhodanese enzyme to detoxify cyanide. Rhodanese converts cyanide and thiosulfate into sulfite and the less toxic thiocyanate.65 Because thiosulfate is produced from the two sulfur amino acids, cysteine and methionine, the success of this enzymatic transformation relies heavily on a diet containing these sulfur amino acids. We hypothesized hill rats may consume a diet lower in sulfur amino acids, leaving them at a disadvantage if they consume cyanide. However, since the products of the enzymatic activity of rhodanese would be difficult to detect via LC-MS, we did not investigate this hypothesis further. Instead, we focused on how woodrats may be dealing with the cyanogenesis products of Prunus.
When woodrats consume Prunus plants, they are likely presented with a mixture of cyanogenic glycosides, mandelonitrile, benzaldehyde, and HCN. We hypothesized that these toxic compounds could possibly pass through the woodrats unmetabolized, and therefore LC-MS data was searched for common positive adducts of cyanogenic glycosides and mandelonitrile. No adducts of amygdalin were identified in the LC-MS data. We did find evidence of mandelonitrile, linarmarin, lotaustralin, prunasin, and dhurrin adducts in some samples (Table 3.4.1). Further investigation revealed there was evidence of many of these compounds in both N. lepida and N. bryanti fecal samples. This was a somewhat expected finding given the broad distribution of cyanogenic compounds across multiple plant families66,67 including Fabaceae, Rosaceae, Grossulariaceae (Ribes)68 and
Euphorbiaceae among others. Prunus (Rosaceae) and Euphorbia (Euphorbiaceae) are also known to comprise a portion of both the hill and the flats diet (Figure 3.1.2). This indicated to us that both N. bryanti and N. lepida are confronted with toxic cyanogenic glycosides in 53 their diets, and both species appear to be passing some of these toxins unmetabolized through their feces. However, metabolic transformations of these toxins may be more significant in maintaining separation of these two woodrat species rather than efficient unmetabolized elimination. To further investigate metabolism of cyanogenic glycosides and compare responses of N. bryanti and N. lepida to diets rich in these compounds would require controlled feeding trials (see section 3.5 Conclusions and future directions).
Table 3.4.1.69 Common Positive Adducts of Cyanogenic Glycosides and Mandelonitrile
Compound Mandelonitrile Linamarin Lotaustralin Prunasin Dhurrin Amygdalin MMa (g/mol) 133.15 247.25 261.27 295.29 311.29 457.43 [M+H]+ 134.16 248.26* 262.28* 296.30* 312.30 458.44
+ [M+NH4] 151.19 265.29* 279.31* 313.33 329.33 475.47
[M+Na]+ 156.14* 270.24 284.26 318.28 334.28 480.42
[M-HCN+Na]+ 129.11 243.21 257.23 291.25 307.25 453.42
+ [M+H-H2O] 116.14* 230.24 244.26 278.28 294.28 440.42
+ [M+H-2H2O] 98.13 212.23 226.25* 260.27* 276.27* 422.41
[sugar- 185.13 185.13 185.13 185.13 185.13 347.27 +b H2O+Na] a MM: Molecular Mass b Sugar for amygdalin is cellobiose because amygdalin is a di-glucoside. * Indicates features we see evidence for in LC-MS data.
Unlike the N. bryanti individual eating Prunus, there was not a cluster of m/z features unique to N. lepida rats eating Rhamnus. This could suggest that N. lepida rats are able to eat Rhamnus plants without severe metabolic costs, but given the larger size of
N. bryanti and their territorial nature, N. lepida may have limited access to Rhamnus plants and therefore don’t have the opportunity to include them in their diets. 54
In our simplified network analysis (Figure 3.3.2), we chose to focus on identifying potential formulas for the masses found in the clusters arising from N. bryanti eating
Rhamnus and N. lepida eating Prunus. Node masses were searched in Metlin (Scripps
Research Institute) online database. Formulas matching the input node mass within 11 ppm tolerance were recorded as were their structures. Crude inspection of formula hits revealed most structures could be assigned to one of the following compound classes: alkaloids, amides, chalcones, chromenes, coumarins, flavanoids/flavonoids, hydrocarbons, imines, iridoid glycosides, lignans, other, prenylated benzoic acids, quinones, saponins, steroids, stilbenes, sugars, terpenoids, and xanthones. Many search results for a single mass returned hits that covered multiple compound classes. To simplify the analysis, we identified the most represented compound class for each potential formula hit for a given mass. While there was little overlap in masses between the hill and flats clusters, many of the masses present in these clusters were correlated to the same compound classes. A large portion of the formula hits were related to compounds that fell into the “other” category. Apart from these compounds, alkaloids appeared most frequently in the hill cluster, while steroids appeared most frequently in the flats cluster. Masses with formulas corresponding to sugars appeared only in the flats cluster (Figure 3.4.2).
55
CompoundCompound Class Class Frequency Frequency in Hillin Hill Cluster Cluster (A) (A) and and Flats Flats Cluster Cluster (B) (B)
HillHill (A) FlatsFlats(B) (B) 20 15
10 Frequency Frequency 5
0 Alkaloids Amides Quionones Hydrocarbons Imines Other Steroids Sugars Imines Other Sugars Amides Steroids Alkaloids Quinones Hydrocarbons
Flavanoids/flavonoids Compound Class
Figure 3.4.2. Frequency of compound classes occurring in the Rhamnus-Hill cluster and the Prunus-flats cluster.
Because the individual woodrats included in this study were not consuming only
Rhamnus or only Prunus, there was likely some overlap in the diets indicating both N. bryanti and N. lepida rats may be ingesting similar mixtures of compounds apart from their major diet components. Therefore, we expected to see some similar metabolites between the two woodrat species as a result of shared diet components in the ubiquitous cluster in the network. While we believe many of the nodes in the ubiquitous cluster are the result of 56 metabolism of shared diet components, our Metlin search reveals that some of these shared diet components may be processed differently between the two species. Because many compound classes are correlated with both the hill and the flats clusters, it is possible that even when N. bryanti and N. lepida rats are consuming the same diet components, they are metabolizing these components differently, resulting in distinct masses correlated to the same compound type. We also identified shared masses between the hill and flats clusters.
Further investigation revealed these masses had retention times that differed by more than the 0.5 min. retention time tolerance, causing them to separate into distinct nodes and fall into distinct clusters (Table 3.4.2). It is possible that these nodes also represent very similar compounds that may be decorated differently due to different metabolic strategies, causing the same masses to have varying polarities and therefore travel at different rates through the column. As Dearing et al.43,44 proposed, specialists or experienced individuals with a diet may be relying more on phase I enzymes over phase II enzymes, causing experienced and inexperienced individuals to metabolize the same compounds differently.
Table 3.4.2. Shared Masses in Different Clusters
Node Label Mass-to-Charge Ratio (m/z) Retention Time (min.) A1 140.1441 2.58 B2 140.1441 6.86 A3 158.1548 2.58 B3 158.1183 1.07 A6 235.2184 9.71 B7 235.2183 13.50 A25 337.1068 7.65 B17 337.1067 6.96 A28 433.3340 14.83 B26 433.3343 15.62
57
To summarize, LC-MS analyses revealed some trends worth pursuing in this system. The network in Figure 3.3.1 revealed shared metabolites between N. bryanti and
N. lepida, unique metabolites for N. bryanti consuming Rhamnus in the hill and N. lepida consuming Prunus in the flats, and unique metabolites for the N. bryanti individual consuming Prunus as part of its diet. The simplified network in Figure 3.3.2 showed distinct clusters for N. bryanti consuming Rhamnus and N. lepida consuming Prunus despite overlap in diet components in both habitats. Further inspection of these m/z features suggested that different metabolic strategies for dealing with toxins are likely significant in driving the separation of N. bryanti and N. lepida despite their close proximity. This finding would best be explored through metabolites identified in other biofluids such as urine and blood.
3.5 Conclusions and future directions
Our goal was to test two main hypotheses: 1) N. bryanti in the hill and N. lepida in the flats will have unique metabolic responses to diets high in different toxins, and 2)
Metabolic fingerprints of N. bryanti and N. lepida will be distinct even when they are consuming the same diet. Both networks revealed distinct clusters for N. bryanti individuals eating Rhamnus and N. lepida individuals eating Prunus, indicating that these woodrat species do have unique metabolic responses to diets rich in different toxins. In the larger, more complex network we saw a small cluster of m/z features resulting from the only hill rat eating Prunus. This suggested that metabolic fingerprints of N. bryanti in the hill and N. lepida in the flats were distinct even when they were both eating Prunus.
However, our data did not reveal a unique cluster for the two flats individuals consuming 58
Rhamnus. Therefore, it is possible flats rats can consume a Rhamnus diet at no/minimal metabolic expense, but perhaps their access to Rhamnus is limited.
Our investigation would benefit from a study done in a more controlled environment. In collaboration with Dr. Marjorie Matocq (UNR-Biology/EECB), we plan to conduct feeding trials in the lab with N. bryanti and N. lepida individuals trapped in the field. Feeding trials in the lab will allow diets to be carefully controlled so we can draw more accurate conclusions about metabolites identified in the woodrats. We will also have the opportunity to analyze the nutritional profile of plants included in the feeding trials.
Plant nutritional profiles will reveal if there are any deficiencies in the diet that may be contributing to different metabolic strategies between N. bryanti and N. lepida. For example, this analysis may reveal if other plants consumed by N. bryanti on the hill are truly lacking in sulfur amino acids, contributing to their inability to metabolize cyanide as efficiently as N. lepida in the flats do. Controlled feeding experiments also enable blood and urine to be collected easily. Blood and urine metabolomic studies will allow us to detect metabolites that have been absorbed into the liver and blood in addition to the metabolites identified in the feces, which may yield more insight into cyanide detoxification. Lastly, we hope to incorporate LC-MS/MS analysis as additional fragmentation data of masses of interest would be invaluable for metabolite/compound identification.
3.6 Supplemental material
Table 3.6.1. Hill Metabolites m/z Labels
Node Label Mass-to-Charge Ratio (m/z) Retention Time (min.) A1 140.1441 2.58 A2 154.1235 1.74 59
A3 158.1548 2.58 A4 160.1339 1.06 A5 174.1497 1.34 A6 235.2184 9.71 A7 249.1977 5.90 A8 251.2136 6.69 A9 251.2136 7.86 A10 265.1927 2.63 A11 265.1927 3.20 A12 265.1929 3.59 A13 267.1612 8.92 A14 267.2081 3.07 A15 278.2495 14.96 A16 279.2086 7.18 A17 281.1879 2.23 A18 288.2634 14.57 A19 294.2469 14.38 A20 296.2605 14.10 A21 305.2662 14.82 A22 310.2405 14.87 A23 312.2590 14.39 A24 328.2558 14.57 A25 337.1068 7.65 A26 337.1069 0.73 A27 365.1383 8.84 A28 433.3340 14.83 A29 440.4129 14.71 A30 636.2544 9.25
Table 3.6.2. Flats Metabolites m/z Labels
Node Label Mass-to-Charge Ratio (m/z) Retention Time (min.) B1 100.1129 2.31 B2 140.1441 6.86 B3 158.1183 1.07 B4 202.0486 6.66 B5 222.0207 0.97 B6 225.1974 14.27 B7 235.2183 13.50 B8 255.1607 10.37 B9 282.2810 14.15 B10 295.1535 10.37 B11 312.2619 14.77 60
B12 312.2631 15.46 B13 314.2783 15.22 B14 315.1244 2.46 B15 315.1247 13.45 B16 315.1249 15.89 B17 337.1067 6.96 B18 337.1069 13.10 B19 337.1070 12.54 B20 337.1071 8.45 B21 337.1074 9.76 B22 338.3437 14.68 B23 415.3235 14.83 B24 416.3284 14.92 B25 431.3181 14.31 B26 433.3343 15.62 B27 439.3600 15.48 B28 445.2984 14.30 B29 447.3135 13.46 B30 449.3290 14.15 B31 449.3291 14.70 B32 463.3082 12.54 B33 463.3084 13.69 B34 463.3094 14.25 B35 479.3033 12.83 B36 563.5549 15.54 B37 563.5549 14.11 B38 758.4742 14.54 B39 1184.6149 14.48 B40 1198.5940 13.99
Table 3.6.3. Ubiquitous Metabolites m/z Labels
Node Label Mass-to-Charge Ratio (m/z) Retention Time (min.) U1 104.1078 0.94 U2 105.0361 6.67 U3 116.0194 0.99 U4 118.0870 0.99 U5 136.0624 1.85 U6 144.0664 3.14 U7 144.1026 1.08 U8 149.0247 15.14 U9 162.1132 0.99 U10 183.1028 8.05 61
U11 184.0649 1.02 U12 197.1185 10.24 U13 235.2175 14.57 U14 235.2180 13.91 U15 235.2181 15.63 U16 235.2182 11.17 U17 235.2182 12.41 U18 235.2182 13.00 U19 235.2182 11.78 U20 235.2184 10.46 U21 253.2181 14.55 U22 258.1117 1.00 U23 263.2386 15.24 U24 265.1930 1.71 U25 279.2338 15.53 U26 280.2651 15.23 U27 281.1878 1.71 U28 282.2811 15.67 U29 284.2969 14.76 U30 296.2611 15.55 U31 298.2773 14.73 U32 300.2995 14.92 U33 304.2582 14.69 U34 314.2731 14.71 U35 320.2588 15.26 U36 337.1068 3.70 U37 337.1069 0.36 U38 337.1069 11.67 U39 337.1069 9.20 U40 337.1070 16.01 U41 337.1071 10.13 U42 337.1072 10.72 U43 353.0810 0.88 U44 356.3293 15.22 U45 365.1385 9.35 U46 365.1385 15.98 U47 365.1386 9.83 U48 365.1386 10.50 U49 365.1386 13.21 U50 365.1386 12.43 U51 365.1387 11.84 U52 365.1388 11.23 U53 381.1119 0.88 U54 399.2542 14.64 U55 410.3296 14.94 62
U56 417.3377 14.60 U57 417.3391 15.36 U58 431.3189 15.03 U59 433.3337 14.22 U60 447.3136 14.20 U61 447.3142 14.75 U62 461.2935 13.46
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American Naturalist. 71
Chapter 4: Seasonal Changes in Neotoma spp. Metabolomes Across the Chemoscape of a Sharp Environmental Gradient
4.1 Introduction
As discussed in Chapter 3, N. bryanti in the hill and N. lepida in the flats are two geographically distinct species whose habitats meet in a hybrid zone in Kelso Valley, CA.
Because Neotoma species are known to consume habitat-specific diets high in different toxins1 and relatively low in nutritional value,2-7 plant diet and the resulting fecal metabolomes were compared across individuals in the hill and the flats. We first confirmed that Rhamnus plants made up a large portion of N. bryanti’s diet in the hill, while N. lepida in the flats consumed mostly Prunus plants. In addition, we found evidence of distinct fecal metabolomic profiles between the woodrat species that is likely a function of their diets being rich in different toxins and differing abilities to process toxins. However, this field study was conducted over a single, short time period. Evidence suggests that plant chemistry and therefore herbivore diet may vary seasonally.8-13 Therefore, to thoroughly investigate diet’s role in ecological adaptation and the observed habitat separation of N. bryanti and N. lepida, seasonal changes in diet resulting in seasonal changes in fecal metabolomes must be considered.
Seasonal and age-related changes in plant chemistry may force an herbivore to seek out new or additional food options during certain times of the year. Additionally, it is possible that availability of Rhamnus or Prunus for N. bryanti and N. lepida, respectively, is limited at certain times due to temperature changes or other environmental factors.
Recent studies suggest that changes in ambient temperature may also affect an herbivore’s 72 ability to metabolize more toxic diet components. This finding may be due to an increase in observed toxicity of plant compounds associated with minor temperature increases, a phenomenon known as temperature-dependent toxicity (TDT).14-16
TDT has been documented both in lab rats15,17 and some species of woodrats,18-20 and may be linked to depressed liver function at elevated temperatures. N. albigula consumes juniper (Juniperus monosperma) which produces monoterpenes such as α- pinene7,21 that are known to be toxic to mammals.22-24 Nevertheless, N. albigula continues to consume varying amounts of juniper depending on the season, with larger amounts being consumed in the winter.6 Feeding trials performed with N. albigula and juniper revealed individuals acclimated to higher temperatures consumed significantly less juniper compared to individuals acclimated to lower temperatures. These results suggest that seasonal changes in juniper consumption observed in N. albigula are mediated by ambient temperature rather than juniper’s availability during different seasons.18 Similarly, a recent study of N. lepida individuals known to consume toxic creosote bush (Larrea tridentata)25,26 found that individuals acclimated to higher temperatures experienced depressed liver function as evidenced by longer clearance times of a hypnotic toxin.19
These results support the hypothesis that TDT may be related to depressed liver function at higher temperatures and motivated further investigation into TDT in N. lepida.
A follow-up feeding trial with this system revealed N. lepida individuals acclimated to higher temperatures consumed significantly less creosote diet than individuals acclimated to lower temperatures. Furthermore, when the same dose of creosote diet was administered to individuals acclimated to either temperature (high or low), individuals at higher temperatures were unable to maintain their body mass, indicating enhanced 73 toxicity.20 Additionally, temperature-mediated changes in hepatic gene expression have been observed in lab rats27,28 and N. lepida individuals,29 providing further support for the hypothesis that TDT may be correlated with liver function. Evidence suggests that woodrats’ ability to consume various toxic diet components may be controlled by liver biotransformation enzymes,30,31 where those who specialize on a diet tend to utilize phase
I functionalization enzymes over phase II clearing enzymes.32,33 Therefore, if temperature effects expression of critical liver biotransformation enzymes, we expect to see unique metabolites result from TDT.
In addition to liver biotransformation enzymes, gut microbiota have also been hypothesized to facilitate toxin ingestion and degradation,4 yet this aspect of detoxification in herbivores has been relatively understudied until recently. Given the genetic diversity of the microbiome,34 it is worth investigating its role in the processing of various plant diets to obtain a complete picture of how Neotoma species detoxify their diets. It has been hypothesized that individuals who have experience with a particular diet will have a more diverse microbiome compared to individuals who are naïve to the same diet. A recent study of N. lepida individuals naïve or previously exposed to creosote bush found that experienced individuals had a uniquely different microbiota than the naïve individuals,35 prompting further investigation of the role of the microbiome in facilitating toxin ingestion.
A follow-up study revealed different levels of gene expression in the metagenomes of experienced individuals compared to those naïve to creosote. Furthermore, administering an antibiotic to disrupt the microbiota in the experienced population resulted in decreased microbial diversity and decreased ability to process the creosote bush diet. Microbial transplant of feces from experienced N. lepida individuals to naïve individuals enhanced 74 their ability to consume the creosote toxins.36 These studies offer strong support for the hypothesis that the microbiome plays a critical role in diet tolerance and detoxification.
Taking these findings into consideration and the sharp transition between habitats and resources, we hypothesized that variation in temperature and gut microbiome may play a critical role in diet selection and/or metabolism. To account for seasonal variation in diet and therefore woodrat fecal metabolomes in N. bryanti and N. lepida, we wanted to revisit the same area and collect fecal samples over multiple time points. In our ongoing collaboration with Dr. Marjorie Matocq (UNR-Biology/EECB), Danny Nielsen (UNR-
Biology/EECB), and Dr. Lora Richards (UNR-Biology/EECB), we used a non-targeted metabolomic approach with complementary network analyses to test the hypotheses that:
1) N. bryanti and N. lepida will have fecal metabolomes that vary seasonally, and 2) N. bryanti and N. lepida metabolic fingerprints will be distinct regardless of seasonal variation in their diets.
4.2 Methods
Sample collection: A total of 95 fecal samples were collected from the hill and the flats throughout the months of March, April, May, July, August, and December for chemical analyses. December samples were from opportunistic collections from only the flats.
Samples were transported in paper envelopes to UNR where we received them for analysis.
Fecal metabolomics: Approximately 200 mg of each sample was transferred to a tared glass centrifuge tube and lyophilized to remove excess water from the samples. After lyophilization, samples were weighed and crushed with a glass rod to create a fine powder.
Samples were then combined with 3 mL of 1:1 CH3OH:H2O, vortexed for 30 seconds, sonicated for 15 minutes, and centrifuged for 15 minutes. A 200 μL aliquot was pulled 75
from each centrifuge tube and combined with 200 μL H2O in a LC-MS vial. The remainder of the liquid extract was filtered into a pre-tared 2-dram vial and dried under nitrogen with reduced pressure to remove CH3OH. The remaining aqueous layer was lyophilized.
LC-MS data (Agilent 6200 TOF with ESI source) was collected for each sample.
Data was processed using MZmine237 and an aligned peak list was generated. The aligned peak list was used to identify m/z features using a m/z tolerance of 0.01 and a retention time tolerance of 0.5 minutes. Chromatographic peaks that fell within these tolerances were grouped into a single m/z feature, allowing m/z features to be compared across different samples. The m/z features contain the m/z, retention time, and peak area data for all samples in which the m/z feature was identified.
Cosine similarity scores network: LC-MS m/z feature data for each fecal sample were converted into unit vectors and cosine similarity scores (dot products) were calculated pairwise for all samples (Chapter 1). Cosine similarity scores range from 0 to 1, where a score of 1 represents identical samples. The cosine similarity cutoff was set to 0.5 after manual inspection of the data. Sample connections with cosine similarity scores less than the user-defined threshold of 0.5 were removed from the network. The network was further filtered to only include edges between samples whose cosine similarity scores appeared in the top 10 scores for the other sample.38 Nodes in this network are fecal metabolomes for each individual, and edges connecting nodes represent more similar fecal metabolomes.
The network was visualized using Cytoscape.39
Weighted Gene Co-Expression Network Analysis (WGCNA): WGCNA has traditionally been used to identify clusters of highly correlated genes which are then correlated to an observed phenotype (Chapter 1).40 We ran an analogous analysis to identify 76 clusters of highly correlated m/z features which could then be correlated to seasonal diet components and gut microbe families using the WGCNA package41 in the statistical software program RStudio (2015). To identify the strongest correlations, LC-MS m/z feature data were filtered to remove singletons and ubiquitous features.42 Additionally, only m/z features with m/z values less than 800 and retention times between 0.5 and 15 minutes were kept to simplify the network.
4.3 Results
Sample collection: Table 4.3.1 summarizes the number of samples collected from the flats and the hill in each collection month. Except for December, all collection months contain samples from both habitats.
Table 4.3.1. Samples Collected from Each Habitat in Each Month
Collection Month Flats Samples Hill Samples March 5 5 April 7 10 May 7 11 July 7 17 August 8 11 December 7 0
Cosine similarity scores network: A single network was created from the filtered data
(Section 4.2) and visualized with two different color schemes. In the first representation, nodes (samples) were colored by the month in which they were collected (Figure 4.3.1). In the second representation, nodes were colored by habitat (hill or flats) (Figure 4.3.2). Edge length connecting two nodes reflects the cosine similarity score between those two nodes where smaller scores (and less similar fecal metabolomes) are connected by longer edges. 77
Nodes with higher cosine similarity scores are clustered together more tightly by shorter edge lengths.
Figure 4.3.1 shows three main clusters. Two clusters (A and B) show samples collected in July and August are highly connected to one another by multiple short edges, indicating these fecal metabolomes are very similar to one another. Some samples collected in March and April are also present in these two clusters, but the clusters are dominated by
July and August samples. There is a separate cluster (C) for samples collected in May.
Samples collected in December are spread throughout the network and do not cluster tightly with other samples.
78
A
B
C
March April May July August December
Figure 4.3.1. Cosine similarity score network where more similar fecal metabolomes are connected by shorter edge lengths. Nodes are colored by month in which the fecal sample was collected. The three main clusters are labeled as A, B, and C.
Figure 4.3.2 shows the same network as in Figure 4.3.1, but the nodes are now colored by the habitat the sample was collected from rather than the month in which the sample was collected. The same three clusters are present. However, there is now a distinct cluster of flats individuals (A), a distinct cluster of hill individuals (B), and the remaining cluster contains individuals from both habitats (C).
79
A
B
C
Flats Hill
Figure 4.3.2. The same cosine similarity score network shown in Figure 4.3.1. Nodes are now colored by habitat in which the fecal sample was collected. The three main clusters are labeled as A, B, and C.
Our collaborators sequenced 16S rRNA for the gut microbial community of the woodrats and the trnL gene to identify diet components in the woodrats’ feces. Analysis of the trnL gene confirmed Prunus is the dominant plant in N. lepida’s diet and Rhamnus is the dominant plant in N. bryanti’s diet. Interestingly, the Phacelia plant genus was more abundant in both N. lepida’s and N. bryanti’s diet during the spring months compared to other times of the year which may account for similar fecal metabolomes between N. bryanti and N. lepida in the month of May. Cosine similarity score networks were also 80 generated for these datasets to bolster our chemical findings (Figure 4.3.3, Figure 4.3.4,
Figure 4.3.5, Figure 4.3.6).
There were 64 samples that had chemical data, 16S rRNA, and trnL data, which provides information about plant diet. After filtering the data according to Section 4.2, the
16S rRNA network had 34 nodes remaining. We identified 4 four main clusters in the 16S rRNA network (labeled A-D). Cluster B shows samples collected in summer months have more similar microbial communities, and cluster C shows samples collected in spring months also have more similar microbial communities (Figure 4.3.3). When nodes are colored by habitat, we see clusters A and D contain individuals predominantly from the hill, while the majority of individuals in clusters B and C are from the flats (Figure 4.3.4).
The trnL network had 60 nodes remaining after the data was filtered. There were no obvious clusters in the trnL networks when clustered by collection month or habitat (Figure
4.3.5 and Figure 4.3.6). We did observe tighter clustering of flats individuals compared to hill individuals, indicating the hill diet may be more diverse (Figure 4.3.6). Lastly, the existing cosine similarity score network for the chemistry data (Figure 4.3.1 and Figure
4.3.2) was filtered to include only the samples that also had 16S rRNA and trnL data
(Figure 4.3.7 and Figure 4.3.8). The same three clusters (A, B, and C) we observed in the full dataset were present. 81
A B
C D
March April
May July August December
Figure 4.3.3. 16S rRNA cosine similarity score network where more similar microbial profiles are connected by shorter edge lengths. Nodes are colored by month in which the fecal sample was collected. The main clusters are labeled as A, B, C, and D.
82
A B
C D
Flats Hill
Figure 4.3.4. 16S rRNA cosine similarity score network where more similar microbial profiles are connected by shorter edge lengths. Nodes are now colored by habitat in which the fecal sample was collected. The main clusters are labeled as A, B, C, and D. 83
March April May July August December
Figure 4.3.5. trnL (plant data) cosine similarity score network where more similar trnL profiles are connected by shorter edge lengths. Nodes are colored by month in which the fecal sample was collected. The are no obvious clusters in this network.
Flats Hill
Figure 4.3.6. trnL (plant data) cosine similarity score network where more similar trnL profiles are connected by shorter edge lengths. Nodes are now colored by habitat in which the fecal sample was collected. The are no obvious clusters in this network.
84
A
B
C
March April May July August December
Figure 4.3.7. Filtered chemistry cosine similarity score network where more similar fecal metabolomes are connected by shorter edge lengths. Nodes are colored by month in which the fecal sample was collected. The three main clusters are labeled as A, B, and C.
85
A
B
C
Flats Hill
Figure 4.3.8. Filtered chemistry cosine similarity score network where more similar fecal metabolomes are connected by shorter edge lengths. Nodes are now colored by habitat in which the fecal sample was collected. The three main clusters are labeled as A, B, and C.
WGCNA: Hierarchical cluster analysis (HCA) with the filtered chemistry data identified
23 modules or clusters of highly connected, co-occurring m/z features. Modules were then correlated to a chemical phenotype to determine the contribution of each sample to the formation of a given module. Module eigengenes (ME) (Chapter 1), or representative metabolomic profiles, were determined for each module. A correlation analysis between
MEs, major plant diet components, and common gut microbe families was performed to identify significant correlations with m/z features in the modules (Figure 4.3.9). The results 86 revealed several significant, positive correlations: black module – Lachnospiraceae; purple module – Lactobacillaceae, Bifidobacteriaceae; salmon module – Lactobacillaceae,
Lachnospiraceae; lightyellow module – Rhamnus plants; lightgreen module –
Bifidobacteriaceae; grey60 module – Eriogonum plants; cyan module –
Verrucomicrobiaceae; magenta module – Bacteroidales; turquoise module –
Pseudomonadaceae, Rhamnus plants. Note that the grey module contains m/z features not present in any of the remaining modules and is not considered for significant trait correlations.
87
ModuleModule-trait−compound Relationships relationships
−0.024 −0.054 0.085 0.035 0.32 −0.033 −0.048 −0.022 0.039 −0.039 0.02 −0.11 −0.025 MEblackMEblack (0.8) (0.7) (0.5) (0.8) (0.01) (0.8) (0.7) (0.9) (0.8) (0.8) (0.9) (0.4) (0.8)
−0.027 −0.069 −0.12 −0.093 0.12 −0.014 0.014 −0.03 0.15 −0.052 −0.08 −0.085 −0.11 MEdarkredMEdarkred (0.8) (0.6) (0.3) (0.5) (0.4) (0.9) (0.9) (0.8) (0.2) (0.7) (0.5) (0.5) (0.4) 1
−0.03 −0.077 0.027 0.14 −0.0058 −0.039 0.02 −0.032 0.21 −0.058 −0.098 −0.044 −0.12 MEredMEred (0.8) (0.5) (0.8) (0.3) (1) (0.8) (0.9) (0.8) (0.09) (0.6) (0.4) (0.7) (0.3)
−0.027 −0.065 −0.086 −0.013 0.096 −0.032 0.0036 −0.018 0.023 −0.052 −0.11 −0.11 −0.067 MEgreenMEgreen (0.8) (0.6) (0.5) (0.9) (0.5) (0.8) (1) (0.9) (0.9) (0.7) (0.4) (0.4) (0.6)
−0.028 −0.067 −0.12 0.064 0.091 −0.044 0.008 −0.012 −0.07 −0.052 −0.12 −0.096 0.2 MEblueMEblue (0.8) (0.6) (0.3) (0.6) (0.5) (0.7) (0.9) (0.9) (0.6) (0.7) (0.4) (0.5) (0.1)
−0.023 −0.055 −0.1 0.1 0.0017 −0.035 −0.048 −0.024 0.035 −0.044 −0.092 −0.1 0.019 MEyellowMEyellow (0.9) (0.7) (0.4) (0.4) (1) (0.8) (0.7) (0.9) (0.8) (0.7) (0.5) (0.4) (0.9)
−0.024 −0.059 −0.071 0.57 −0.0093 −0.035 0.36 −0.027 −0.026 −0.047 −0.11 0.039 0.2 MEpurpleMEpurple (0.8) (0.6) (0.6) (1e−06) (0.9) (0.8) (0.004) (0.8) (0.8) (0.7) (0.4) (0.8) (0.1) 0.5 −0.028 −0.072 0.2 0.34 0.31 −0.021 0.084 −0.032 0.16 −0.054 −0.12 −0.057 −0.072 MEsalmonMEsalmon (0.8) (0.6) (0.1) (0.005) (0.01) (0.9) (0.5) (0.8) (0.2) (0.7) (0.3) (0.7) (0.6)
−0.018 0.026 −0.09 −0.15 −0.05 0.021 −0.05 −0.021 −0.11 0.0098 −0.087 0.15 −0.038 MEdarkturquoiseMEdarkturquoise (0.9) (0.8) (0.5) (0.2) (0.7) (0.9) (0.7) (0.9) (0.4) (0.9) (0.5) (0.2) (0.8)
−0.025 0.035 −0.069 −0.12 −0.042 −0.039 −0.074 −0.028 −0.12 0.86 −0.054 0.071 0.059 MElightyellowMElightyellow (0.8) (0.8) (0.6) (0.4) (0.7) (0.8) (0.6) (0.8) (0.3) (6e−20) (0.7) (0.6) (0.6)
−0.028 0.16 0.042 −0.17 −0.069 −0.026 −0.08 −0.028 −0.15 0.02 −0.043 −0.049 0.13 MEtanMEtan (0.8) (0.2) (0.7) (0.2) (0.6) (0.8) (0.5) (0.8) (0.2) (0.9) (0.7) (0.7) (0.3) −0.03 −0.005 0.075 −0.21 −0.079 −0.049 −0.063 −0.034 −0.19 0.024 −0.11 −0.11 −0.094 MEgreenyellowMEgreenyellow (0.8) (1) (0.6) (0.09) (0.5) (0.7) (0.6) (0.8) (0.1) (0.9) (0.4) (0.4) (0.5) 0 −0.025 −0.054 −0.024 −0.044 −0.066 −0.041 0.054 −0.029 −0.11 −0.043 −0.1 −0.046 −0.021 MEpinkMEpink (0.8) (0.7) (0.9) (0.7) (0.6) (0.7) (0.7) (0.8) (0.4) (0.7) (0.4) (0.7) (0.9) −0.021 −0.026 −0.048 −0.055 −0.057 −0.034 −0.0054 0.025 −0.12 −0.026 −0.031 −0.076 −0.076 MEroyalblueMEroyalblue (0.9) (0.8) (0.7) (0.7) (0.7) (0.8) (1) (0.8) (0.4) (0.8) (0.8) (0.5) (0.6) −0.0028 −0.062 −0.12 −0.11 −0.04 −0.039 0.57 −0.027 −0.15 −0.045 −0.0044 0.091 −0.0023 MElightgreenMElightgreen (1) (0.6) (0.3) (0.4) (0.8) (0.8) (1e−06) (0.8) (0.2) (0.7) (1) (0.5) (1) −0.03 −0.076 0.0038 −0.073 −0.078 −0.049 −0.006 −0.034 −0.14 −0.058 −0.0044 0.28 0.014 MEgrey60MEgrey60 (0.8) (0.6) (1) (0.6) (0.5) (0.7) (1) (0.8) (0.3) (0.6) (1) (0.02) (0.9)
−0.028 −0.034 −0.037 −0.12 −0.042 −0.043 −0.028 −0.032 −0.017 −0.026 0.069 −0.099 0.013 MElightcyanMElightcyan (0.8) (0.8) (0.8) (0.4) (0.7) (0.7) (0.8) (0.8) (0.9) (0.8) (0.6) (0.4) (0.9) 0.42 −0.031 −0.039 −0.15 0.14 −0.026 −0.092 −0.034 0.11 −0.056 −0.017 0.019 −0.13 −0.5 MEcyanMEcyan (6e−04) (0.8) (0.8) (0.2) (0.3) (0.8) (0.5) (0.8) (0.4) (0.7) (0.9) (0.9) (0.3) −0.0037 −0.046 −0.046 0.02 −0.055 −0.047 −0.081 −0.028 0.24 −0.03 0.0056 −0.13 −0.16 MEmidnightblueMEmidnightblue (1) (0.7) (0.7) (0.9) (0.7) (0.7) (0.5) (0.8) (0.05) (0.8) (1) (0.3) (0.2) −0.034 −0.031 0.0016 −0.065 0.24 −0.041 −0.068 0.76 0.17 −0.053 0.1 0.14 −0.1 MEmagentaMEmagenta (0.8) (0.8) (1) (0.6) (0.06) (0.7) (0.6) (4e−13) (0.2) (0.7) (0.4) (0.3) (0.4) −0.031 0.58 −0.067 0.2 −0.045 −0.049 −0.07 −0.012 −0.11 0.39 0.094 −0.093 −0.0045 MEturquoiseMEturquoise (0.8) (5e−07) (0.6) (0.1) (0.7) (0.7) (0.6) (0.9) (0.4) (0.001) (0.5) (0.5) (1) −0.029 −0.06 0.071 0.13 −0.045 −0.041 −0.014 0.0076 0.23 −0.026 0.13 −0.099 0.1 MEbrownMEbrown (0.8) (0.6) (0.6) (0.3) (0.7) (0.7) (0.9) (1) (0.07) (0.8) (0.3) (0.4) (0.4) −0.033 0.0032 0.11 0.12 −0.066 −0.05 −0.082 0.086 −0.068 −0.0067 0.16 −0.031 −0.12 MEdarkgreenMEdarkgreen (0.8) (1) (0.4) (0.3) (0.6) (0.7) (0.5) (0.5) (0.6) (1) (0.2) (0.8) (0.4) −1
0.013 −0.047 0.079 0.12 −0.016 −0.023 −0.028 −0.085 −0.48 0.0014 −0.048 0.13 0.35 MEgreyMEgrey (0.9) (0.7) (0.5) (0.3) (0.9) (0.9) (0.8) (0.5) (7e−05) (1) (0.7) (0.3) (0.004)
Pinus Prunus Rhamnus Phacelia Pinus Prunus Eriogonum OxalobacterLactobacillus Bacteroides Pseudomonas Phacelia Bifidobacterium Rhamnus Verrucomicrobium Eriogonum Bacteroidales LactobacillaceaeLachnospiraceae OxalobacteraceaeLachnospiraceae.UCG.010Bifidobacteriaceae Pseudomonadaceae ErysipelotrichaceaeErysipelotrichaceae.UCG.004 Verrucomicrobiaceae Figure 4.3.9. Module-trait relationships. Rows are module eigengenes, and columns are common gut microbe families and major diet components in each habitat. The top number in each cell is the correlation and the bottom number is the p-value. The grey module is reserved for m/z features that cannot be grouped into the other modules and is not considered for significant correlations.
88
4.4 Discussion
Our analyses suggest there is seasonal variation in fecal metabolomes within a species as well as distinct fecal metabolomes between species. The cosine similarity score network colored by month of sample collection (Figure 4.3.1) resulted in three distinct clusters. Cluster A contained highly connected nodes from the months of July and August with a few connections to samples from the months of March and April. Cluster B showed a similar pattern, with a few more connections to samples from March and April. While we expected, and observed to some extent, that samples from the same time of year would cluster more tightly than samples from other seasons, the presence of two distinct clusters with similar patterns of connectivity between months within the clusters was curious.
Cluster C was consistent with our hypothesis that samples from one month would cluster more tightly together. Overall, it initially appeared there was not clear separation of diets by month except for the month of May.
Due to the trend observed in Chapter 3 where N. bryanti and N. lepida had distinct fecal metabolomes despite samples being from the same collection period, we explored the possibility that fecal metabolomes were more different between N. bryanti and N. lepida than they were between collection months. Using the same network shown in Figure 4.3.1, we instead colored nodes by woodrat species/habitat (Figure 4.3.2). It became apparent that Cluster A was the result of samples from flats individuals (N. lepida), cluster B was the result of samples from hill individuals (N. bryanti), and cluster C contained a combination of flats and hill samples.
Comparing the two networks (Figure 4.3.1 and Figure 4.3.2) provided more insight into the effects of seasonal variation in diet for N. bryanti and N. lepida. We now 89 understand cluster A as containing samples from N. lepida individuals in the flats where samples from the later months (July and August) are clustered more tightly together, suggesting N. lepida rats do have distinct fecal metabolomes in the latter half of the year compared to the first half. Similarly, cluster B contains samples from the N. bryanti individuals in the hill where samples from the later months (July and August) are clustered more tightly together. As with the flats cluster (cluster A), this suggests that N. bryanti rats do have distinct fecal metabolomes in the latter half of the year.
Cluster C is unique in that it essentially contains only samples from May, and hill and flats samples are clustering tightly together, a trend not previously observed. This is a unique case of hill and flats samples not remaining distinct from one another. Therefore, this suggested to us that the diets between these two species must be converging in May resulting in more similar fecal metabolomes. Additionally, these findings have implications in terms of the way these diets are being metabolized. In Chapter 3, we found evidence of
N. bryanti and N. lepida rats metabolizing the dominant plant in the adjacent habitat differently than the native rat species. However, a single cluster observed for these two species in the month of May indicates these rats may be eating plants present in both habitats that are not particularly toxic to either species, and therefore distinct metabolic pathways are not required for detoxification.
The samples from December are all N. lepida individuals in the flats and do not cluster tightly with each other or any other samples. This could be due in part to the decreased sample size for the month of December as well as the absence of any samples collected from hill individuals during this time (Table 4.3.1). In addition, since woodrats 90 are pack rats and tend to cache food throughout the year, diets in the winter months can be from plants stored at different times of the year.
In addition to seasonal variation in fecal chemical profiles, we found evidence of seasonal shifts in microbial communities between habitats and woodrat species. The cosine similarity score network for 16S rRNA data resulted in four distinct clusters. When nodes were colored by collection month (Figure 4.3.3), we saw that samples collected in the summer clustered together (cluster B) as did samples collected in the spring (clusters C and
D), supporting a seasonal shift in microbial communities. When nodes were colored instead by habitat (Figure 4.3.4), we saw that clusters A and D contained mostly individuals from the hill while clusters B and C contained mostly individuals from the flats, supporting a seasonal shift in microbial communities between habitats as well. Cluster A containing hill individuals was unique in that samples from all seasons were clustering together. We hypothesized that this could be the result of greater diet diversity throughout the year for
N. bryanti individuals in the hill. This hypothesis was further supported by the trnL cosine similarity score network colored by habitat (Figure 4.3.6). We observed tighter clustering of flats individuals compared to hill individuals, indicating N. bryanti individuals in the hill consume a more diverse diet resulting in more diverse microbial communities that produce unique fecal metabolomes.
We performed WGCNA to investigate how groups of m/z features from fecal metabolomes could be related to plant diet and microbial communities. Several modules contained m/z features that were positively correlated with various gut microbe families and plant samples, indicating these m/z features may be directly related to plant chemistry and/or gut microbe metabolism. The turquoise module was significantly correlated with 91
Pseudomonadaceae and Rhamnus plants. This could indicate this bacterial family is critical for Rhamnus metabolism, and m/z features in this module are being investigated to annotate these features as unmetabolized phytochemicals or metabolites from gut microbe transformations.
4.5 Conclusions and future directions
With this study, we aimed to test two hypotheses: 1) fecal metabolomes vary seasonally for N. bryanti and N. lepida, and 2) metabolic fingerprints of N. bryanti and N. lepida will remain distinct regardless of any seasonal variation in their diets. Networks generated from a cosine similarity scores analysis provided support for both hypotheses, with samples from the same time of year clustering more tightly together and samples from one woodrat species clustering separately from samples from the other woodrat species.
Samples from the months of May and December were the most intriguing. The only occurrence of N. bryanti and N. lepida clustering together was in the month of May. We found support for seasonal changes in gut microbial communities between the habitats and woodrat species as well as seasonal shifts in plant diet with the Phacelia genus becoming more abundant in the spring for both habitats. These findings provide strong support for the critical role the gut microbiome plays in detoxifying the toxic plants encountered by N. bryanti and N. lepida.
Future work on this project aims to provide more clarity on the cause of seasonal fecal metabolomes. Feeding trials in a controlled environment will be conducted and we will obtain blood, urine, and feces metabolomes for all individuals. Collecting blood and urine will allow us to investigate differences in metabolite compositions between N. bryanti and N. lepida when they are presented with the same diet. A controlled environment will 92 also allow us to identify woodrat-microbe metabolites from fecal samples. Where plant chemistry and fecal chemistry contain the same m/z features, we will be able to infer that these m/z features are plant toxins being eliminated unmetabolized by the woodrat. This will help narrow our investigation to metabolic transformations that are likely tied to the gut microbial community. In this way, we can begin to understand how diet and the gut microbiome are contributing to the observed habitat separation between N. bryanti and N. lepida in their natural habitats.
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Chapter 5: Quantifying the Chemotypes of Two Toxic Asclepias spp. and Their Effects on the Specialist Herbivore, Danaus plexippus
5.1 Introduction
Chapters 3 and 4 detailed the investigation of specific plant-herbivore interactions from a mammalian herbivore’s (the woodrat’s) perspective. In collaboration with Dr. Mark
Hunter (University of Michigan - Dept. of Ecology and Evolutionary Biology) and Dr.
Leslie Decker (Stanford University - Dept. of Biology), our group has been examining the relationship between a toxic plant genus, Asclepias (milkweed), the insect herbivore
Danaus plexippus (monarch butterfly), and its parasite, Ophryocystis elektroscirrha.
Monarch butterflies specialize on milkweed plants, which are known to produce a class of toxic cardiac glycosides known as cardenolides.1-7 Cardenolide content and concentration varies for different species of milkweed plants. For this study, we chose to focus on just two milkweed species, A. curassavica and A. incarnata. A. curassavica is known to produce high concentrations of cardenolides while A. incarnata produces considerably lower concentrations of these toxic compounds.8,9 These plants also produce a variety of secondary metabolites from different compound classes besides cardenolides. Other phytochemicals found in A. curassavica include acyclic compounds,10 alkaloids,11 flavonoids,12 and terpenes13 (reviewed in Sundararajan and Kodura 201414). In A. incarnata, pregnane glycosides,15 phenolic compounds, and flavonoids16 have been identified.
Despite the variety of plant secondary metabolites produced by milkweed species, the majority of studies on this system have been targeted in nature, focusing only on 98 cardenolides as they have demonstrated anti-parasitic properties for monarch butterflies.17-
19 Monarchs consuming milkweed species that maintain high cardenolide concentrations have displayed greater tolerance to parasite attack through reduced rates of infection and parasite growth.17-21 Furthermore, studies have shown monarchs experience greater protection from parasite attack when they consume high dietary concentrations of cardenolides before or during infection,18 supporting the hypothesis that milkweed plant chemistry is critically involved in the monarch’s immune response.
In Chapter 4, we considered temperature-dependent toxicity (TDT) where an increase in plant compound toxicity is observed with minor temperature increases.22-24
Increased ambient temperatures may also be linked to either positive or negative effects on immune function through altered enzymatic activity.25-30 However, many of these studies have not yet considered the effect increased ambient temperatures may have on diet quality in their system and therefore immune function. In the same vein, increasing levels of CO2 can alter both plant chemistry and nutritional quality.31-34 In milkweed plants, elevated levels of CO2 are known to cause considerable declines in both cardenolide concentrations and nutritional quality of the plant.35,36 Therefore, to thoroughly investigate the effects of milkweed plant chemistry on monarchs and their immune response, it is critical to consider these environmental effects.
There is strong evidence that cardenolides mediate interactions between monarchs and their parasite O. elektroscirrha, and that plant secondary metabolites can have an effect on an herbivore’s immune function and response.37-43 However, this evidence is from a targeted approach, focusing only on cardenolides and failing to consider minor plant components which may be critical on their own or act synergistically.44 Whole plant 99 chemotypes of different milkweed species have yet to be identified and linked back to the observed ecological responses of monarchs, which would provide a more complete assessment of the role milkweed have on monarch immune response.
This study examines the whole plant metabolomes of A. curassavica and A. incarnata and the potential effects elevated CO2 levels may have on these metabolomes
(Decker et al. in prep). Our collaborators grew A. curassavica and A. incarnata at both ambient and elevated CO2 levels for chemical evaluation. Monarchs were infected with the
O. elektroscirrha parasite, fed plant tissue from one of the four experimental conditions, and several ecological response variables were measured (Decker et al. in prep). The focus of this chapter is on the chemistry of the two milkweed species at ambient and elevated
CO2 levels. A non-targeted metabolomic approach and network analyses were used to quantify chemotypes of A. curassavica and A. incarnata grown at ambient and elevated
CO2 levels and relate these chemotypes to monarch response variables. We tested the hypotheses that: 1) A. curassavica and A. incarnata would have unique chemotypes, 2)
The chemotypes of A. curassavica and A. incarnata would remain distinct even when these plants were grown under ambient or elevated CO2 conditions, and 3) The quantified chemotypes could be correlated to specific monarch responses resulting in a more detailed understanding of chemistry’s role in mediating milkweed-monarch-parasite interactions.
5.2 Methods
Sample collection: We received 241 samples of A. curassavica (120 grown under ambient
CO2 conditions; 121 grown under elevated CO2 conditions) and 240 samples of A. incarnata (120 grown under ambient CO2 conditions; 120 grown under elevated CO2 conditions) from our collaborators. Of the 481 samples we received, four were excluded 100 from our analyses due to excessive loss of material, leaving a total of 477 samples for our analyses (Table 5.2.1).
Table 5.2.1 Plant Sample Distribution
Species Ambient CO2 Elevated CO2 A. curassavica 119 119 A. incarnata 119 120
Plant metabolomics: Extraction conditions for this study were adapted from Santos
Pimenta et al.45 and optimized for the milkweed system. Approximately 200 mg of each ground plant sample was transferred to a glass centrifuge tube and combined with 3 mL of a mixture of 25% KH2PO4 buffer (90 mM, pH = 6.0) in D2O and 75% CD3OD with 0.1%
TMS. Samples were vortexed for 30 seconds, sonicated for 15 minutes, and centrifuged for
15 minutes. Approximately 0.7 mL of the supernatant was filtered into a NMR tube and the remaining supernatant was filtered and transferred to a pre-tared 2-dram vial and stored.
1H-NMR spectra were collected on a MR-400 MHz NMR spectrometer.
MestReNova software (Mestrelab Research, Spain) was used to process the 1H-NMR data.
Each spectrum was first aligned to the residual methanol solvent peak at 3.31 ppm. Spectra were then phase-corrected using the Global method, baseline-corrected using the Whittaker
Smoother method, and binned/integrated (Peaks method) over 0.04 ppm bins ranging from
0.5 – 12.0 ppm. Residual solvent and water peaks were removed by cutting, and the spectra were normalized to 100 units using the Total Area method. These data were then exported for further network analyses.
Cosine similarity scores network: A cosine similarity scores analysis was conducted on
1H-NMR data to see if A. curassavica and A. incarnata would cluster separately based on 101 their plant metabolomes. 1H-NMR peak area data for each plant sample were converted into a unit vector to obtain pairwise cosine similarity scores for all samples (Chapter 1).
Scores range from 0 to 1, where 0 represents no similarity between samples, and 1 represents identical samples. The cosine similarity score cutoff was set to 0.95 after manual inspection. A cutoff of 0.95 is unusually high for this type of analysis, however, it may be necessary when working with samples that have high overlap in phytochemical mixtures.
Sample connections with cosine similarity scores less than the user-defined threshold of
0.95 were removed from the network. The network was then filtered to only include edges between plant samples whose cosine similarity scores appeared in the top 10 scores for the other sample.46 Nodes in this network are plant metabolomes for each individual, and edges connecting nodes represent more similar plant metabolomes. The network was visualized using Cytoscape.47
WGCNA: WGCNA has been a powerful tool for analyzing large genomic datasets.48,49
Given the large amount of metabolomic data we had, we envisioned using WGCNA to identify clusters of highly correlated 1H-NMR peaks which are grouped into modules. 1H-
NMR peaks that fall into the same module are likely from the same compound as they tend to co-vary together across multiple samples. Furthermore, modules (groups of chemical shifts) that are highly correlated to one another may yield more structural information about a single compound or groups of compounds that also co-vary across multiple samples.
Therefore, WGCNA and the resulting modules were used to quantify chemotypes of A. curassavica and A. incarnata and relate these chemotypes to ecological variables and effects on monarchs.
5.3 Results 102
Cosine similarity scores network: A cosine similarity scores network was created from the 1H-NMR data of A. curassavica and A. incarnata plant samples grown at either ambient or elevated CO2 levels. To test the appropriateness of the cutoff (0.95) for edges to be kept between samples, nodes in the network were first colored by plant species (Figure 5.3.1).
Clusters A and B represent clusters of A. incarnata and A. curassavica plant metabolomes, respectively. The smaller surrounding clusters are a mix of A. curassavica and A. incarnata samples. To investigate the role CO2 levels may be playing in the formation of the clusters, nodes in the network were then colored by CO2 levels (Figure 5.3.2). The two main clusters
(A and B) contain a mix of samples grown at ambient and elevated CO2 levels, and no obvious patterns were found in the smaller outside clusters. Lastly, nodes in the network were colored by plant species and CO2 conditions (A. curassavica, ambient CO2; A. curassavica, elevated CO2; A. incarnata, ambient CO2; A. incarnata, elevated CO2) (Figure
5.3.3), revealing that phytochemical mixtures of both A. curassavica and A. incarnata remain highly similar and correlated regardless of CO2 levels.
103
A
B
A. curassavica A. incarnata
1 Figure 5.3.1. Cosine similarity scores network of H-NMR data from A. curassavica and A. incarnata plant samples grown at either ambient or elevated CO2 levels. Nodes in the network represent plant metabolomes and are colored by plant species.
104
A
B
Elevated CO2
Ambient CO2
1 Figure 5.3.2. Cosine similarity scores network of H-NMR data from A. curassavica and A. incarnata plant samples grown at either ambient or elevated CO2 levels. Nodes in the network represent plant metabolomes and are colored by CO2 level.
105
A
B
A. curassavica, ambient CO2 A. curassavica, elevated CO2 A. incarnata, ambient CO2 A. incarnata, elevated CO2
1 Figure 5.3.3. Cosine similarity scores network of H-NMR data from A. curassavica
and A. incarnata plant samples grown at either ambient or elevated CO2 levels. Nodes
in the network represent plant metabolomes and are colored by plant species and CO2 levels.
WGCNA: Hierarchical cluster analysis (HCA) identified 9 modules of co-varying 1H-
NMR chemical shifts (Figure 5.3.4). Module eigengenes (MEs) were determined for each module. A correlation analysis between MEs and plant species (A. curassavica and A. incarnata) and CO2 level (ambient and elevated) revealed several statistically significant correlations (Figure 5.3.5). The red, brown, and green modules were positively correlated with plant species, while the magenta and pink modules were negatively correlated with plant species. The red module was also positively correlated with CO2 levels. The pink, brown, and green modules were negatively correlated with CO2 levels. 106
A ClusterCluster Dendrogram Dendrogram
1.0
0.8
0.6
Height Height 0.4
0.2
0.0
as.dist(dissTom) Module colors Module Colors fastcluster::hclust (*, "average")
B EigenvalueEigenvalue Dendrogram dendrogram 1.4
1.2 MEred
1.0
0.8
0.6 MEturquoise
0.4
MEgreen MEbrown MEpink 0.2 MEblue MEmagenta MEblack MEyellow
Figure 5.3.4. A) HCA of chemical shift values resulting in 9 modules. B) Eigenvalue dendrogram showing related modules.
107
ModuleModule−-compoundtrait Relationships relationships
0.24 0.39 MEredMEred (2e−07) (2e−18) 1
−0.54 −0.085 MEmagentaMEmagenta (3e−38) (0.06)
−0.79 −0.18 MEpinkMEpink (2e−104) (1e−04) 0.5
0.2 −0.42 MEbrownMEbrown (1e−05) (5e−22)
MEgreen 0.7 −0.35 MEgreen (2e−72) (4e−15) 0
0.043 0.043 MEturquoiseMEturquoise (0.3) (0.3)
0.0023 0.038 MEblueMEblue (1) (0.4) −0.5 MEblack −0.021 −0.049 MEblack (0.7) (0.3)
MEyellow −0.03 0.019 MEyellow (0.5) (0.7)
MEgrey 0.045 0.026 −1 MEgrey (0.3) (0.6)
CO22 Species CO Species
Figure 5.3.5. Module-trait relationships. Rows are module
eigengenes, and the traits of interest are plant species and CO2 levels. The top number in each cell is the correlation and the bottom number is the p-value.
Further investigation of the modules significantly correlated with plant species revealed the following: samples with high MEs for the red, brown, and green modules were
A. curassavica plant samples, and samples with high MEs for the magenta and pink modules were A. incarnata plant samples (Table 5.3.1). For the modules significantly correlated with CO2 levels, we determined: samples with high MEs for the red module 108
were grown under elevated CO2 conditions, and samples with high MEs for the pink, brown, and green modules were grown under ambient CO2 conditions.
Table 5.3.1 Module Chemical Shifts
Module Color Chemical Shifts Plant Species Red 1.30 2.42, 2.74, 2.82 3.57-3.85 4.01-4.17 5.41 Brown 0.86-2.78 4.30 A. curassavica 6.61, 6.81 7.21-7.41 Green 1.14 5.37-5.85 6.37-6.85 7.01-7.17, 7.65-8.13 Magenta 3.89 6.25, 6.45 7.57, 7.93 A. incarnata Pink 5.17 6.29, 6.49, 6.93, 6.97 7.61, 7.73, 7.81
WGCNA was also used to correlate the detected modules with ecological response variables measured by our collaborators. Ecological response variables included monarch mass, survival, pupal score, larval period, pupation, life, melanization, total spores, bound
PO, total hemocytes, total granulocytes, total plasmatocytes, total oenocytoids, and total spherulocytes (Table 5.3.2). Figure 5.3.6 summarizes the immune response variables.50
Significant correlations were identified for all ecological response variables previously listed with the exception of pupation, total spores, and total oenocytoids (Figure 5.3.7).
109
Table 5.3.2. Description of Ecological Response Variables
Measurement Definition/Significance Monarch mass Mass of adult monarch. Survival Survival of monarch during experiment. Pupal score Measurement of pupal case darkening that is related to intensity of infection. Larval period Length of the larval period. Pupation Length of the pupal period. Life Length of life. Melanization Measurement of monarch’s ability to melanize antigens. Total spores Total spore load as a measure of infection severity. Bound PO Total bound phenoloxidase (PO), which is a measure of humoral immunity and closely related to melanization. Total hemocytes Measure of cellular immunity, related to encapsulation ability. Total granulocytes A type of hemocyte related to phagocytosis and encapsulation. Total plasmatocytes A type of hemocyte related to aggregation and encapsulation. Total oenocytoids A type of hemocyte related to PO production. Total spherulocytes A type of hemocyte whose immunity function is uncharacterized.
110
Insect Immune Response
Humoral Responses Cellular Responses
Bound PO is stored in Antimicrobial peptides hemocytes, catalyzes Hemocyte-driven responses melanization Plasmatocytes Coagulation/melanization Granulocytes of hemolymph Spherulocytes Oenocytoids Reactive intermediates
Capsules may melanize through oenocytoids Encapsulation: Phagocytosis: Hemocytes bind Single cell engulfs to parasitoids foreign entity
Most common hemocytes: Plasmatocytes Granulocytes
Figure 5.3.6. Summary of insect immune response. Variables discussed in the text are shown in blue, bold text.
111
ModuleModule-trait−compound Relationships relationships
MEred 0.11 −0.0058 0.088 0.1 −0.023 0.098 0.15 0.012 −0.039 −0.095 −0.14 −0.15 0.029 0.055 MEred (0.02) (0.9) (0.05) (0.03) (0.6) (0.03) (7e−04) (0.8) (0.4) (0.04) (0.002) (7e−04) (0.5) (0.2) 1
MEmagenta −0.14 −0.058 0.023 0.14 0.012 −0.16 −0.071 0.035 0.079 0.068 0.022 0.081 −0.065 0.097 MEmagenta (0.003) (0.2) (0.6) (0.002) (0.8) (4e−04) (0.1) (0.4) (0.08) (0.1) (0.6) (0.08) (0.2) (0.04)
MEpink −0.19 −0.11 0.1 0.1 0.046 −0.23 −0.16 0.079 0.011 0.032 0.001 0.13 −0.067 0.059 MEpink (3e−05) (0.01) (0.03) (0.03) (0.3) (7e−07) (3e−04) (0.08) (0.8) (0.5) (1) (0.004) (0.1) (0.2) 0.5
MEbrown 0.1 0.031 −0.051 −0.087 0.04 −0.036 −0.018 −0.04 −0.11 0.066 0.14 0.061 0.0058 −0.11 MEbrown (0.02) (0.5) (0.3) (0.06) (0.4) (0.4) (0.7) (0.4) (0.02) (0.2) (0.002) (0.2) (0.9) (0.02)
MEgreen 0.24 0.043 −0.043 −0.077 0.014 0.21 0.06 −0.057 −0.03 −0.1 −0.059 −0.14 0.014 −0.076 MEgreen (2e−07) (0.4) (0.3) (0.09) (0.8) (5e−06) (0.2) (0.2) (0.5) (0.02) (0.2) (0.002) (0.8) (0.1) 0
MEturquoise −0.076 0.0081 0.068 −0.079 0.047 −0.05 0.04 −0.02 −0.034 −0.071 −0.055 −0.073 −0.051 −0.052 MEturquoise (0.1) (0.9) (0.1) (0.09) (0.3) (0.3) (0.4) (0.7) (0.5) (0.1) (0.2) (0.1) (0.3) (0.3)
MEblue 0.049 −0.062 −0.096 −0.14 −0.0021 0.11 −0.028 −0.066 0.15 −0.15 −0.12 −0.14 −0.086 −0.12 MEblue (0.3) (0.2) (0.04) (0.002) (1) (0.02) (0.5) (0.2) (7e−04) (0.001) (0.01) (0.002) (0.06) (0.007)
−0.5 MEblack −0.04 0.013 −0.14 −0.13 0.0091 0.0027 −0.13 −0.044 0.2 0.0031 0.019 0.028 −0.017 −0.022 MEblack (0.4) (0.8) (0.002) (0.005) (0.8) (1) (0.004) (0.3) (8e−06) (0.9) (0.7) (0.5) (0.7) (0.6)
MEyellow 0.052 −0.009 −0.093 −0.12 0.013 0.076 −0.047 −0.067 0.13 −0.071 −0.042 −0.039 −0.065 −0.074 MEyellow (0.3) (0.8) (0.04) (0.007) (0.8) (0.1) (0.3) (0.1) (0.004) (0.1) (0.4) (0.4) (0.2) (0.1)
−0.014 0.022 −0.024 −0.0034 −0.032 0.029 0.032 −0.031 0.14 −0.031 −0.036 −0.083 −0.02 3e−04 −1 MEgreyMEgrey (0.8) (0.6) (0.6) (0.9) (0.5) (0.5) (0.5) (0.5) (0.002) (0.5) (0.4) (0.07) (0.7) (1)
life
pupation Life total_spore pupal_scorelarval_period melanization monarch_masssurvival_binarySurvival Pupation total_hemocyte total_granulocyte total_oenocytoid Pupal score Bound PO total_spherulocyte MelanizationTotal spores total_plasmatocyteoenocytoids Monarch mass Larval period bound_po_last_readingTotal hemocytes Total granulocytesTotal Total plasmatocytesTotal spherulocytes Figure 5.3.7. Module-trait relationships. Rows are module eigengenes, and columns are ecological response variables of monarchs. The top number in each cell is the correlation and the bottom number is the p-value. The grey module is reserved for chemical shift data that cannot be grouped into the other modules and is not considered for significant correlations.
5.4 Discussion
The analyses presented confirm distinct chemotypes for A. curassavica and A. incarnata despite known overlap in their phytochemical composition (Figure 5.3.1). A cosine similarity scores analysis also revealed the chemotypes of these two plant species remain distinct regardless of CO2 conditions the plant was grown under (Figure 5.3.2). 112
After confirming A. curassavica and A. incarnata have unique chemotypes, we aimed to use WGCNA to quantify the differences between these two plant species.
WGCNA revealed the red, brown, and green modules were significantly correlated with A. curassavica plants. We hypothesized that these modules contained chemical shift values related to cardenolides since A. curassavica plants are known to maintain high cardenolide concentrations (Table 5.3.1). Through comparison of the 1H-NMR spectra of the ME samples for the red, brown, and green modules, we confirmed that some characteristic cardenolide peaks were identified by the modules (Figure 5.4.1). However, we noticed the green module contained chemical shift values further downfield than what is typical of cardenolides. Further inspection revealed chemical shift values in the green module were consistent with kaempferol 3-O-β-glucopyranoside,16 which was supported by the recent review of the chemical constituents in A. curassavica leaves.51 We also identified chemical shift values consistent with asclepin15 and 12-O-benzoylsarcostin.15 As a result, we adjusted our hypothesis to account for the various compound classes contributing to the A. curassavica chemotype. The observation that WGCNA identified chemical shift values from other compound classes besides cardenolides highlights the importance of a non-targeted approach in these types of studies.
113
Cardenolide O 12-O-benzoylsarcostin Kaempferol 3-O-β- O 23 O 21 glucopyranoside 28 3’ 22 OH 18 HO 4’ 20 27 22 2’ 12 23 O 18 21 1 17 20 8 1’ 11 12 HO 9 O 2 5’ 19 13 26 24 OH 7 H 16 11 1 9 14 25 19 13 17 6’ 16 3 2 1 9 OH 14 6 10 8 15 H H 10 O 2 15 5 4 3 5 7 10 8 3 OH OH O 1’’ OH 7 4 H 6 O HO 5 2’’ 4 6 5’’ 4’’ 6’’ 3’’ OH
OH OH Asclepin
20 21 4’ 5’ H-1’’ H-17 H-12 O 3’ 19 OH 23 22 24 25 1’ 18 O 26 2’ 1 17 16 15 H 30 14 2 13 3 O 12 4O 11 5 O 10 6 O HO 9 7 8 O 27 28 3 29 O
H-2’, H-6’ H-22 H-3’, H-5’, H-8
2
H-27 H-25, H-26, H-27 H-19 H-11 H-11
1
8.0 7.5 7.0 6.5 6.0 5.5 4.5 4.0 3.53.0 2.5 2.0 1.5 1.0 f1 (ppm) Figure 5.4.1. Overlay of ME spectra for the red (top), green (middle), and brown (bottom) modules. ME samples for the modules shown are all A. curassavica plants. Peaks consistent with compounds identified in A. curassavica are labeled. Solvent peaks have been removed for clarity.
WGCNA also revealed the magenta and pink modules were significantly correlated with A. incarnata plants. Therefore, we hypothesized that these modules contained chemical shift values more strongly associated with A. incarnata plant chemistry. Because
A. incarnata is known to maintain relatively low cardenolide concentrations, our findings 114 suggested the modules may be detecting another important compound class or mixture of compounds. Table 5.3.1 shows the chemical shift values in the magenta and pink modules.
The peaks contained in these modules are more downfield in the NMR spectrum whereas the peaks contained in the modules significantly correlated with A. curassavica plants had greater coverage of the 1H-NMR spectrum. We began our investigation by looking at the
1H-NMR spectra for the MEs for the magenta and pink modules. A crude examination of the data suggested a flavonoid-like structure was present in this sample. In 2003, Sikorska reported the isolation and identification of a number of flavonoids in A. incarnata leaves.16
Through comparison with the existing literature, we were able to identify characteristic peaks of quercetin 3-O-β-glucopyranoside, a flavonoid (Figure 5.4.2). These results indicated the magenta and pink modules were detecting flavonoid chemical shift data specific to A. incarnata plants, and therefore we assign the magenta and pink modules as the A. incaranta chemotype.
115
Quercetin OH 3’ 4’ OH 2’ 1 8 1’ HO O 2 5’ 7 6’ 6 3 5 4 OH O
H-8 H-6 2 H-2’, H-6’
H-5’
1
8.0 7.5 7.0 6.5 6.0 5.5 4.5 4.0 3.5 f1 (ppm)
Figure 5.4.2. Overlay of ME spectra for the magenta (top) and pink (bottom) modules. ME samples for the modules shown are all A. incarnata plants. Peaks consistent with previously reported 1H-NMR data of quercetin16 are labeled. Solvent peaks have been removed for clarity.
A stacked plot of representative A. incarnata and A. curassavica plant samples reveals the subtle differences in their phytochemical mixtures (Figure 5.4.3). Peaks in the magenta and pink modules appear to have greater peak intensity in A. incarnata samples compared to A. curassavica samples. Likewise, peaks in the peaks in the red, brown, and green modules appear to have greater peak intensity in A. curassavica samples compared 116 to A. incarnata samples. Remarkably, WGCNA was sensitive enough to distinguish between two structurally similar flavonoids and associate them with different chemotypes.
In this manner, we confirmed that A. incarnata and A. curassavica have unique, quantifiable chemotypes.
Red Module A. curassavica 5 Elevated CO2
Green Module
A. curassavica 4 Ambient CO2
Brown Module A. curassavica 3 Ambient CO2
Magenta Module A. incarnata 2 Ambient CO2
Pink Module A. incarnata 1 Ambient CO2
8.5 8.0 7.5 7.0 6.5 6.0 5.5 4.5 4.0 3.53.0 2.5 2.0 1.5 1.0 0.5 f1 (ppm) Figure 5.4.3. Comparison of representative A. curassavica and A. incarnata phytochemical mixtures. Peaks contained within the corresponding modules are highlighted with the module color. Solvent peaks have been removed for clarity.
The WGCNA showed the red module was significantly correlated with elevated
CO2 levels (Figure 5.3.5). Because the red module was also significantly correlated with
A. curassavica plants, we hypothesized the peaks in the red module (Table 5.4.1) corresponded to compounds in A. curassavica that were being affected by increased CO2 levels. The pink, brown, and green modules were significantly correlated with ambient CO2 117 levels. The pink module was also significantly correlated with A. incarnata, indicating the peaks in the pink module are characteristic of A. incarnata plant chemistry at ambient CO2 levels. Finally, the brown and green modules were also significantly correlated with A. curassavica plants. Therefore, the peaks contained in these two modules are representative of A. curassavica chemical constituents at ambient CO2 conditions. Comparing the significant module correlations with both plant species and CO2 conditions provides an additional layer of resolution for the chemotypes defined above (Table 5.4.1) and supports our hypothesis that the chemotypes of A. curassavica and A. incarnata would remain distinct regardless of CO2 conditions.
Table 5.4.1 Summary Table of Significant Module Correlationsa
Module Color Chemical Shifts Plant Species CO2 Level Red 1.30 A. curassavica Elevated 2.42, 2.74, 2.82 3.57-3.85 4.01-4.17 5.41 Brown 0.86-2.78 A. curassavica Ambient 4.30 6.61, 6.81 7.21-7.41 Green 1.14 A. curassavica Ambient 5.37-5.85 6.37-6.85 7.01-7.17, 7.65-8.13 Magenta 3.89 A. incarnata N/A 6.25, 6.45 7.57, 7.93 Pink 5.17 A. incarnata Ambient 6.29, 6.49, 6.93, 6.97 7.61, 7.73, 7.81 a Plant chemotypes are further resolved by CO2 levels.
118
Our last goal was to correlate A. curassavica and A. incarnata chemotypes with ecological response variables measured by our collaborators (Decker et al. in prep). Our results revealed significant correlations between the two plant chemotypes and the ecological response variables. Generally, the A. curassavica chemotype was correlated with increased growth and survival of the monarchs, with those fed the A. curassavica diet having overall higher body mass and longer lives. However, the A. curassavica chemotype was also generally correlated with a weakened immune response as evidenced by negative correlations with total hemocytes, total granulocytes, total plasmatocytes, and total spherulocytes. Two unique correlations existed for the A. curassavica chemotype. The red module (A. curassavica at elevated CO2 levels) was significantly correlated with melanization. Because elevated CO2 levels are known to decrease cardenolide concentration and nutritional quality of milkweed plants,35,36 the conditions described by the red module leave the monarch more susceptible to parasitic attack resulting in a suppressed ability to melanize antigens. While the red and green modules of the A. curassavica chemotype were correlated with a suppressed immune response, peaks in the brown module were correlated with increased granulocytes. In Figure 5.4.1, we saw peaks in the brown module were representative of cardenolide, 12-O-benzoylsarcostin, and asclepin structures, peaks in the green module were representative of flavonoid and cardenolide structures, and peaks in the red module were a mix of these compound classes along with sugar features. Therefore, when cardenolide concentrations are not decreased due to elevated CO2 conditions, as is the case for the brown module, we see an enhanced immune response through elevated granulocyte concentrations. This finding supports 119 existing evidence that cardenolides have anti-parasitic properties for monarch butterflies.17-
19
In contrast to the A. curassavica chemotype, the A. incarnata chemotype was correlated with lower monarch masses and lifespans and higher concentrations of plasmatocytes (pink module) and spherulocytes (magenta module). This indicated to us that monarchs feeding on a diet of A. incarnata at ambient CO2 levels had an enhanced immune response. Interestingly, the pink module was significantly correlated with enhanced melanization indicating something unique about the plant chemistry described in this module may be protective to monarchs. This observation is critical because much of the emphasis in milkweed-monarch interactions has been placed on cardenolides, yet the
A. incarnata chemotype is more reflective of flavonoid content which may be critically involved in monarch immune response as well. The power of the analyses presented above is that they comprise a hypothesis-generating approach, serving as a guide for discovery of patterns that may not be immediately obvious or expected.
5.5 Conclusions and future directions
This study utilized a non-targeted metabolomic approach to quantify the chemotypes of A. curassavica and A. incarnata. We tested three hypotheses: 1) The two plant species would have unique chemotypes, 2) The chemotypes of A. curassavica and A. incarnata would remain distinct from one another when grown under ambient or elevated
CO2 conditions, and 3) The quantified chemotypes could be correlated to specific monarch immune responses. Cosine similarity scores networks supported the first two hypotheses, with separate clusters arising for A. curassavica and A. incarnata plant samples. WGCNA indicated modules of 1H-NMR chemical shifts that were distinctly different between the 120 two species and allowed the quantification of the two plant chemotypes. A module-trait correlation analysis correlated the plant chemotypes to various ecological response variables and indicated both cardenolide and flavonoid-like structures may be affecting monarch immune response. Specifically, we found evidence that the flavonoids in the A. incarnata chemotype may enhance monarch immune function whereas previous studies on monarch-milkweed interactions have focused only on the importance of cardenolides.
Future work on this project will include collection of LC-MS and eventually
MS/MS data for all plant samples included in this study, which will aid in confirming the presence and identity of other secondary metabolites, like flavonoids, in these phytochemical mixtures. We also plan to run module-trait correlation analyses on subsets of the chemistry data corresponding to different monarch treatment groups. The aim will be to identify stronger correlations between plant chemotypes and monarch response variables that are specific to uninfected vs. infected monarch individuals to enhance our understanding of plant chemistry’s role in protecting monarchs from parasitic attack.
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Chapter 6: Conclusions and Future Work
6.1 Conclusions and future work
The field of chemical ecology is concerned with understanding the role of plant secondary metabolites, which serve a critical purpose in mediating interactions between organisms and often exist in complex mixtures that act as deterrents or attractants for various herbivores.1-3 Metabolomic approaches have become increasingly important in chemical ecology studies as they strive for comprehensive metabolic characterization of organisms. Unfortunately, many metabolomic studies have been targeted in nature, focusing only on a specific compound or subset of compounds.4 Besides being time- consuming, targeted metabolomic approaches do not consider minor plant secondary metabolites or synergistic effects on herbivores.5 The tremendous advancements made in analytical methods coupled with non-targeted metabolomic approaches have enabled researchers to begin a more thorough investigation of phytochemical diversity and its ecological consequences, yet proper handling of large metabolomic datasets remains an issue. The overarching goal of this dissertation work was to adapt current molecular networking approaches that have found utility in large genomic studies and apply these approaches to the chemical metabolomic datasets our group has been building for a variety of systems.
Phytochemical diversity is labile
Given the importance of phytochemical diversity in mediating plant-herbivore interactions,3,6-9 many hypotheses about the evolution of phytochemical diversity have been put forth but have not been adequately tested. Two popular hypotheses concerning 129 the evolution of phytochemical diversity are the co-evolutionary arms race hypothesis7 and the screening hypothesis.10 Weighted gene co-expression network analysis (WGCNA) of
1H-NMR data collected for various plant species in the hyper-diverse Radula clade generated support for the screening hypothesis (Chapter 2). We found that phytochemical diversity does not parallel increased species diversity as the co-evolutionary arms race hypothesis suggests. Instead, some individual chemical traits/structural features appear to be conserved across the phylogeny while other structural features are more randomly distributed. Conservation of some structural features and lability of others suggests plants are maintaining high phytochemical diversity through branching biosynthetic pathways to produce products or precursors that will protect the plant.
Toxic phytochemicals maintain separation of two mammalian herbivores
The importance of structural features/mixtures of features compared to phytochemical diversity as a whole was echoed in our work with two mammalian herbivores, Neotoma bryanti and N. lepida. N. bryanti and N. lepida are two closely related species living in close proximity to one another and have specialized on habitat-specific diets high in various toxins and with low nutritional value.11-17 Our non-targeted fecal metabolomic analysis revealed N. bryanti and N. lepida have unique metabolic responses to their toxin-rich diets. Additionally, we observed distinct metabolic responses of N. bryanti and N. lepida when these individuals consumed the major diet component in the adjacent habitat (Chapter 3). We hypothesize the anthraquinones in N. bryanti’s Rhamnus- dominated diet and the cyanogenic glycosides in N. lepida’s Prunus-dominated diet are driving the separation of these two otherwise closely related species. In Chapter 4, we described findings of a seasonal shift in plant diet and gut microbial communities between 130 the habitats and Neotoma species, suggesting the gut microbiome may serve a critical role in detoxifying the diet toxins previously mentioned.
Non-targeted metabolomic approach suggests additional phytochemical involved in insect herbivore immune response
Toxic cardenolides found in milkweed species (Asclepias) have long been regarded as the major compound class imparting protection for monarch butterflies (Danaus plexippus) against the parasite Ophryocystis elektroscirrha.18-27 Our non-targeted metabolomic approach with complementary 1H-NMR network analyses allowed us to consider other compounds/compound classes that may be mediating the monarch-parasite interaction. WGCNA with 1H-NMR data yielded quantifiable plant chemotypes that were correlated to various ecological monarch response variables. Preliminary structural assignments to plant chemotypes revealed flavonoids, in addition to the suspected cardenolides, are important mediators between monarch butterflies and the O. elektroscirrha parasite (Chapter 5), a finding that has been overlooked in previous targeted approaches.
The work presented in this dissertation shows the adaptability of existing molecular networking approaches and may serve as a template for other researchers looking to implement powerful data visualization tools for large datasets in a variety of fields.
Although we have made great progress in understanding the importance of phytochemical diversity in a number of systems, all projects discussed in this dissertation are ongoing.
The 1H-NMR-based projects (Chapters 2 and 5) would greatly benefit from complementary liquid chromatography – mass spectrometry (LC-MS) metabolomic studies as this would allow us to confirm or adjust predictions made about structural features identified in the 131
1H-NMR data. We plan to collect the necessary LC-MS data in addition to LC-MS/MS data for all projects discussed in this dissertation since the resulting fragmentation patterns will further support our structural assignments. Future work for the woodrat projects specifically (Chapters 3 and 4) include controlled feeding trials that will enable us to further investigate specific woodrat and microbe metabolites. We have seen the importance of phytochemical diversity and how it can mediate herbivore-herbivore interactions as well as herbivore-parasite interactions. Continued work on these projects will result in more detailed examination of phytochemical diversity trends and further refinement of methods for handling large metabolomic datasets.
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