The Pennsylvania State University

The Graduate School

Intercollege Graduate Degree Program in Ecology

THE PHYSIOLOGICAL ECOLOGY OF AN ENTOMOPATHOGENIC FUNGUS:

EXAMINING HOST-NICHE THROUGH METABOLIC FOOTPRINTING OF

OPHIOCORDYCEPS UNILATERALIS S.L.

A Thesis in

Ecology

by

Colbie J. Reed

© 2017 Colbie J. Reed

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

August 2017

ii

The thesis of Colbie J. Reed was reviewed and approved* by the following:

David P. Hughes

Assistant Professor of Entomology and Biology

Thesis Advisor

John Tooker

Associate Professor of Entomology

Extension Specialist

David Eissenstat

Professor of Woody Plant Physiology

Chair of the Ecology Intercollege Graduate Degree Program

*Signatures are on file in the Graduate School.

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TABLE OF CONTENTS

List of Tables………………………………………………………………………………………………………… iv

List of Figures…………………………………………………………………………….…….……………………. v

Acknowledgements……………………………………………………………………………….………………… viii

Chapter 1. BACKGROUND AND FOUNDING QUESTIONS. 1

1.1 Physiological Ecology and Evolution of Host-parasite Relationships and Host-niche Development. 1

1.2 Objectives, Core Questions and Thesis Statement. 3

1.3 Featured Figures. 5

Chapter 2. EXOMETABOLOMIC SIGNATURES OF ENTOMOPATHOGENIC FUNGI IN VITRO… 7

2.1 Abstract. 7

2.2 Introduction. 7

2.2.1 In vitro cultivation of Entomopathogenic Fungi.

2.2.2 Exometabolomics ‒ an ‘Omics’ Approach to “Hacking” the Host-parasite “Cloud”.

2.2.3 Understanding a Parasite and its Host-niche through its Ecophysiological Requisites.

2.3 Materials and Methods. 15

2.3.1 Liquid Fermentation of Blastoconidia for Longitudinal and End-point Assays.

2.3.2 Exometabolomics – Longitudinal and End-point Assays.

2.3.3 Assay of Secreted Protein.

2.3.4 Measurement of Supernatant pH and Differential Calculation.

2.3.5 Figure Generation and Statistics of Exometabolomic Data.

2.4 Results. 19

2.4.1 Longitudinal Exometabolomics of O. kimflemingae, in vitro.

2.4.2 High Productivity with Very Little Input: Lag Phase and Underlying Compositional Dynamics…

2.4.3 Temporally and Magnitudinally Distinguished Metabolites.

2.4.4 Characterization of Substratum-utilization Phenotype by Macronutrient Category.

2.4.5 Follow-up: Supplementation of Ionic Cofactors, Chelation and Phenotype Rescue.

2.4.6 Comparison of Two Hypocrealean Species Using Longitudinal Exometabolomics.

2.4.7 Ophiocordyceps kimflemingae – Extracellular Physical and Chemical Perturbation with Objective of…

2.5 Discussion. 40

2.6 Featured Tables. 46

2.7 Featured Figures. 48

Chapter 3. Summary and Concluding Statements. 85

3.1 Summary of Findings and Implications for Pathogenic Fungi. 85

3.2 Conservation of Biological Interfaces and Broader Impacts. 88

3.3 Future Work. 90

3.4 Featured Figures. 93

Appendix A: Supplemental Results. 97

Appendix B: Supplemental Tables and Figures. 100

Bibliography. 127

iv

LIST OF TABLES

2.0 Grace’s Insect Medium ‒ Official Formulation. 46

2.1 Minimal Media Formulations. 47

B2.0 Experimental Designs ‒ End-point Studies. 100

B2.1 Hexmap Base Index Key ‒ End-point Studies. 102

B2.6 Hexmap Base Index Key ‒ Longitudinal Infraspecific Comparisons. 116

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LIST OF FIGURES

1.0 Model of Fundamental Niche for a Ruderal, Specialist Entomopathogenic Fungus. 5

2.0a Photographic Diagram of the Life Cycle of O. unilateralis s.l. 48

2.0b Schematic of in vitro Growth Assays. 49

2.1 Longitudinal Exometabolomic Data for O. kimflemingae, in vitro. 51

2.2a Primary Carbon Sources. 53

2.2b TCA Cycle and its Contributors. 54

2.2c Focal Amino Acids. 55

2.2d Urea Cycle and Pyrimidine Synthesis. 56

2.2e Vitamins – Pantothenate and Precursors. 57

2.2f Vitamins – Nicotinate Metabolism Requires Pyridoxine. 58

2.3a End-point Exometabolomic Metaprints for Supplementation and Chelation of Trace Ions. 60

2.3b Heatmap of Metacluster Bases Underlying Suprahexagonal Metaprints. 61

2.3c Metacluster Bases, Node Assignments and Pathway Enrichment Index. 62

2.4 Comparative Metaprints for Two Hypocrealean Species. 64

2.5 Longitudinal Exometabolomic Data for O. camponoti-floridani, in vitro. 66

2.6 Metatracks ‒ Tracing Metabolic Footprints through Time. 68

2.7a Infraspecific Dual-plot – Comparative Carbon-source Utilization. 70

2.7b Infraspecific Dual-plot – Differential Nitrogen-source Utilization. 71

2.7c Infraspecific Dual-plot – Differential Vitamin Utilization. 72

2.7d Infraspecific Dual-plot – Differential Use of Precursors. 73

2.8 End-point Exometabolomic Signatures of Asparagine- and Methionine-fortified Substrata. 75

2.9 End-point Exometabolomic Signatures of Signaling Molecule-mimic. 77

2.10 End-point Exometabolomic Signatures of Altered Substrata Starting pH. 79

2.11 End-point Exometabolomics Confirms Viability of Minimal Medias. 81

2.12 Perturbations Reveal Exometabolomic Signatures of Life and Death. 83

3.0 Model Amendment ‒ Proposed Host-niche and Host-parasite Exchange. 93

3.1 Ecological Model Amendment (full). 95

B2.2 End-point Exometabolomic Trace Ion/Macromineral Hexmap Topologies. 103

B2.3a End-point Exometabolomic Signatures – Trace Ion/Macromineral Supplementation. 105

B2.3b-c End-point Trace Ion Supplementation – Differential pH and Secreted Protein. 106

B2.3d End-point Exometabolomic Signatures – Trace Ion Chelation/Titration. 107

B2.3e-f End-point Trace Ion Chelation/Titration – Differential pH and Secreted Protein. 108

vi

B2.3g End-point Exometabolomic Signatures – Trace Ion Rescues. 109

B2.3h-i End-point Trace Ion Rescues – Differential pH and Secreted Protein. 110

B2.4 Hexagonal Map with Select Labels – Infraspecific Comparisons. 112

B2.5 Longitudinal Exometabolomic Hexmap Topologies. 114

B2.7a-b Asparagine and Methionine ‒ Differential pH and Secreted Protein. 118

B2.7c-d Dibutyryl-cAMP ‒ Differential pH and Secreted Protein. 119

B2.7e-f Adjusted Starting pH ‒ Differential pH and Secreted Protein. 120

B2.7g-h Minimal Medias ‒ Differential pH and Secreted Protein. 121

B2.8 Morphological Changes Between Various Perturbations. 123

B3.0 Traditional SEIR Model and Adjusted Model for a Parasitoid. 125

vii

PREFACE

It is with a reasonable amount of pride and exasperation that I present this completed document. The pursuit

of this achievement was ultimately motivated by my desire to better understand things at their most intimate

and complex level, while using tools that limit bias through their exactness. All of the material and findings

presented in this work were conceived, completed, assembled and polished within the course of six months.

The importance of this preceding statement is based upon just that: the timeline within which it was

completed, as well as the circumstances of which. I had hoped to finish this project to a greater extent of

completion. As a result, I will look forward to hearing of someone else having picked-up this work where I

have left off, as I believe this is an exciting topic worthy of continued study.

Through this process, I have learned a great deal about science and about myself. Despite it all, my passion

for science remains as strong as ever.

viii

ACKNOWLEDGEMENTS

To those that got it all started and stuck around to finish it:

I would like to thank my friends and family. And also to apologize to them for ever wandering into academia.

You all have tolerated my nonsense ramblings, my work-infused acute stress disorder, my incessant need

to find a meaning in every tiny, little detail. I have neglected some of your best advice, but, in those moments

of prodigal epiphany, you still lent an ear, reminding me of the kindness and empathy that exists but is

sometimes far too seldom-expressed in this world. I have a tendency to avoid people, but you all have

continued to remind me that, not only are they necessary, but people are also what make science great.

Anybody can have a brilliant idea, but it’s the people that make them possible.

To the things that made it easier in between, I’d like to thank:

Job offers from not-academia. [Good] coffee. [Great] wine. True crime podcasts. Dream-delivered ‘aha!’

moments. Antihistamines. The internet. Educated guesses. Action Camp. The Brain Pickings of Maria

Popova. Friendly internet trolls. Science drama. Free textbooks. Hot sauce. Snarky statisticians. The long

list of scientific “Mavericks & Heretics” picked apart and put on display by Information Is Beautiful

(https://goo.gl/A8qiiD), as well as their pariah contemporaries. The President’s Council of Advisors on

Science and Technology (PCAST) and their 2016 formal report, Forensic Science in Criminal Courts:

Ensuring Scientific Validity of Feature-Comparison Methods. Biophysics and Physical Biology. Elegant

questions. Uncertainty.

To the people, places and things that made it physically (and metaphysically) possible:

These include, firstly, the Huck Institutes’ Metabolomics Core Facility; without their passion and dedication

to the Facility’s cause and for the impact that it has upon its users campus-wide, the University’s reputation

for research would not be what it is today. I must direct special thanks to the laboratory of Dr. Manuel Llinás

for assistance in completing of this project. I have much gratitude for the wonderful faculty and staff of Penn

State University, and thank the Huck Institutes of the Life Sciences, the Center for Infectious Disease

Dynamics, and the CIDD Graduate Student Association for their having originally provided me the

confidence to pursue academia, as well as the Coalition of Graduate Employees for pursuing ‘a seat at the

table’ for graduate students throughout the Penn State community. I also am extremely grateful for the

Penn State Microbiome Center, as their interdisciplinary team-centric efforts and inclusive attitudes are

inspiring and act to foster young professionals, early career scientists and students across fields interested

in microbial communities. Finally, I would like to thank Dr. Peter Hudson for his continued inspiration,

support and encouragement, and for being a kindred spirit in always seeking to push science forward. May

you never allow—in yourself or in those you influence—extinguishing of the inner restlessness and

determination that, together, prove continuously precipitant of the compulsion to pursue difficult questions.

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DEDICATION

For my niece and nephew,

who will grow up being told [by me, at least] that they may pursue any passion, any question or any problem

if they are so compelled.

For those who work to forget the moments in which friends could not find words to wield or when heroes

became hollow.

Only individuals have a sense of responsibility.

—Nietzsche

1

Chapter 1. Background and Founding Questions.

1.1 Physiological Ecology and Evolution of Host-parasite

Relationships and Host-niche Development.

Across all phyla, organisms must adapt to the biotic and abiotic challenges1 of their environment

to maintain fitness and produce viable offspring. Examples of biotic challenge include competition

for resources, predators, and parasites, the influences of which can result in morbidity, loss of

fecundity or death. Abiotic challenges can be the cause of similar ends, but are solely physical or

chemical in nature, examples of which include turbulence, temperature, pH and radiation. Each

type of environmental challenge—independently and together—sculpts both the fundamental and

realized niche of organisms [1]. Adaptations to environmental stresses can be quite diverse and

are often directly connected to the evolutionary histories of species [2]. This biological influence

of evolutionary momentum and the magnitude of its sway in determining an organism’s realized

niche can be illustrated by the examination of a single abiotic environmental feature. To illustrate,

some regions around the globe are noted for their high levels of radiation, whether it be ultraviolet

or infrared (thermal); however, additional facets of their geography play significant roles in

determining how organisms might effectively adapt to these levels of radiation. In polar regions,

solar radiation (ultraviolet radiation) is often ameliorated through the adaptation of white fur, which

selectively enhances the insulation of thermal energy in the far infrared while simultaneously

conferring high-reflectance of ultraviolet spectral wavelengths [3], [4]. Although a drastically

different landscape from the arctic circle, solar radiation must also be combatted by organisms

indigenous to arid regions near the Earth’s equator. A distinct example, the Saharan silver ant

(Cataglyphis bombycine), sports an immensely-reflective ‘silver’ cuticle, which allows it to

maintain its fitness by optimizing its reflectance of thermal radiation, enhancing its emissivity2 [5];

this results in accelerated heat loss, ideal for maintaining lower body temperatures in such a hot

environment. Ultimately, the composite organism and its respective life history traits are the

product of all abiotic environmental features. Thus, the lack of organismal translatability between

niches reminds us that organisms, themselves, represent much larger resultant “wholes”, an n-

dimensional hypervolume, and, therein, are greater than the sum of their individual “parts”, the n-

number of axes comprising said hypervolume [6]. Moreover, this exemplifies the hypothesis that

1 Biotic environmental challenges/stresses are of a living nature; examples of biotic environmental challenges include organisms imposing resource competition or predation pressure. Contrastingly, abiotic environmental challenges/stresses are non-living and are often described as physical or chemical in nature; examples of these include temperature, humidity, pH, or physical/chemical effects that result from biotic activity. 2 Emissivity describes the efficacy with which a surface is able to emit thermal radiation.

2

examining only one environmental factor while in pursuit of ecological-relevance should be

exercised with caution.

Traditionally, the study of ecology views organisms within their environments as distinct,

independent elements within a larger web of interactions. However, an endosymbiont’s

experienced environment is constituted by the viscera and tissues of another animal: a uniquely

biotic environment. This “living” environment also possesses abiotic characteristics which

contribute to a diverse, dynamic landscape, such as the traditional physical factors of pH and

temperature. Naturally, an endosymbiotic lifestyle poses unique challenges for the organisms

living it, but it also provides offsetting benefits. A significant difference to the abiotic repertoire

experienced by endosymbionts is that the host behavior within the external environment can

drastically impact the survival and fitness of the hosted endosymbiont. For example, the

movements of a host can dictate the dispersal success of a given endosymbiont, depending on

the mode through which the hosted organism must exit/dispatch its offspring and how the host

may or may not inhibit this evacuation. Contrastingly and by definition, parasites are dependent

upon their host for resources; ample access to nutrients and optimal microclimate are provided

by and are a function of the host rather than solely that of the parasite. It is the traits characteristic

to these resource-consumer relationships which define a parasite’s host-niche [7]. Due to the

nature of these relationships, it is not exceptional or far-removed to posit that the examination of

host-niche through a lens based upon ecological stoichiometry3,4,5 could result in a more holistic

realization of the fluid economy between organisms. In this way, it may also provide systemic

insight and predictive power to the ways in which we understand the modes and mechanisms of

host-parasite interactions.

The host-parasite system addressed in this work features a ruderal, specialist

entomopathogenic fungus6 (see Chapter 2: Introduction for system and life cycle). To better

understand the ecological context of the organism being examined, I generated a model of its

fundamental and realized niche (Figure 1.0). As a specialist, this parasite has adopted its host as

3 Ecological Stoichiometry ‒ “The balance of multiple chemical substances in ecological interactions and processes, or the study of this balance. Also sometimes refers to the balance of energy and materials.” [254]. An approach which encompasses and integrates biological stoichiometry and nutritional ecology. 4“Biological Stoichiometry is the study of balance of energy and multiple chemical elements in biological systems ranging from molecules to ecosystems. It focuses on key cellular and physiological structures and functions and their associated bio- chemical demands while considering evolutionary change primarily from the perspective of individual fitness.” [255] 5 Nutritional Ecology is the integrated study of organisms, their ecological environments, and respective nutritional determinants of interactions between organisms and their environments. [71] 6 Entomopathogenic fungi (EPFs) are fungal pathogens that are known to infect terrestrial arthropods, either opportunistically or in a highly-specific, obligative fashion, for any or all phases of the fungal species’ life cycle.

3

a primary habitat (i.e., realized host-niche), but, due to the nature of the relationship, experiences

obstacles to transmission, which requires a second habitat outside of the host (i.e., realized

environmental niche). This physical separation between the point of propagation and that of

maturation within the host is a trait of many pathogens; however, in a unique twist characteristic

of only parasitoids, this parasite demonstrates a latency period that extends beyond its incubation

period (i.e., its host only becomes infectious after death). In address of this challenge, this

organism and closely-related species have specially-adapted to optimize the utility of its host by

controlling its host’s movement and orientation upon the climax of infection. This extended

phenotype is understood to enhance dispersal of infectious spores [8], and it has been posited

that this must require observation of abiotic environmental features experienced by both the

parasite and the host (e.g., circadian/circannual rhythm feedback/initiation, or sexual structure

germination determined by degree days) [9], [10]. The model generated, here, will be referenced

as necessary throughout this work to emphasize key findings within the ecological context of this

host-parasite relationship.

1.2 Objectives, Core Questions and Thesis Statement.

To best evaluate and characterize this complex host-parasite system, I began by asking specific

questions and defining key objectives. To understand the role of the parasite in this dynamic

process, a reductionist model was implemented where I removed the host altogether and worked

in vitro to define the nutritional requirements of the parasite. However, the host and the ecological

context of the system were kept in-mind when designing experiments and making the necessary

biological interpretations. My specific objectives were: a) determine key nutritional and select

physical requisites of the parasite, Ophiocordyceps kimflemingae; b) examine the physiological

responses of the parasite occurring under various disturbance regimes; c) utilize the acquired

data to formulate a selective minimal media for improved research methods; and d) utilize the

acquired data to improve an ecological model of the system.

Core orienting questions were derived to provide insight into the rationale behind the

aforementioned objectives. Specifically, are there certain nutritive requirements that make a

parasite a parasite? If so, what factors predispose an organism to parasitism? Does an organism’s

ecology determine the potential for parasitism or is it solely a function of biotic forces? Based on

these conceptual questions and the previously described experimental objectives, as well as the

published literature germane to this project, a formal hypothesis was derived and general,

facilitating warrants were proposed. These two warrants considered in the development of the

proceeding thesis statement were: 1) cross-talk is continuous between the host and the parasite

4

(either indirect or direct); and 2) because of the inherent nature of the parasitic lifestyle, much of

this “cross-talk” must be either about or is constituted by “food”. As a result, the formal thesis

statement was formulated as follows: the nutritional needs of Ophiocordyceps kimflemingae can

provide insight into its ecophysiology and how the parasite successfully colonizes and influences

its host.

5

1.3 Featured Figures.

Figure 1.0 Model of Fundamental Niche for a Ruderal, Specialist

Entomopathogenic Fungus.

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Figure 1.0 Model of Fundamental Niche for a Ruderal, Specialist

Entomopathogenic Fungus. This figure is intended to illustrate the hypothetical, proposed fundamental and realized niche of O.

kimflemingae, a ruderal specialist entomopathogenic fungus. Here, the environment is represented as that

which is most proximal to the host-parasite relationship. The host is annotated as a biotic microcosm within

the larger habitat. Inside of the host exists the realized host-niche, which, after infection maturity and host

death, expands to nearly comprise the host as a biological whole; however, only select microclimates exist

within the dynamic spatiotemporal mosaic of the larger environment that will effectively facilitate the

parasite’s fitness and spore dispersal after the host has been fixed and consumed. These constitute this

organism’s realized environmental niche. This conceptualization intends to ease the ways with which these

variables and states involved in this complex system are handled, ultimately, to enable a more complete

understanding of what is known and what still needs to be examined; this applies to this and future projects

involving this host-parasite system.

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Chapter 2. Exometabolomic signatures of entomopathogenic fungi

in vitro: characterization of individual substratum-utilization

phenotypes and interspecific comparison.

2.1 Abstract.

Difficulties persist in the study of host-parasite relationships, and these challenges can be

exacerbated when these pathogenic relationships involve fungi as their causative agents. With

the use of high-resolution technologies, the interstitial spaces between these organisms become

more accessible. Here, a highly-specialized fungal parasite of ants, Ophiocordyceps

kimflemingae, was used to examine the ecophysiological value underlying a parasite’s

biochemical aura, and to determine whether the chemical context surrounding a pathogen might

in some way implicate its corresponding host-niche. Changes in the growth medium were

characterized with the use of targeted exometabolomics and were further extrapolated into a form

of biological heuristic using self-organizing maps. O. kimflemingae was determined to be

immensely resourceful, demonstrating an impressive auxoautotrophic capacity. Only a small

fraction of media metabolites was detectably removed, namely, select sulfur-containing amino

acids and sources of versatile carbon backbones. Additionally, several compounds—indicators of

sulfur metabolism, DNA replication and cell growth—were suggested to be actively-produced by

the organism, despite ample provision in the medium. A single comparative analysis introducing

a second phylogeographically-relevant species demonstrated some similar and other unshared

nutritional requirements, evidencing evolutionary and ecological significance attributable to their

respective host-niches. As demonstrated here, exometabolomics-driven approaches and similar

techniques enable the study of layered or obscured organismal relationships. Ultimately, they

provide opportunities to develop more holistic, translatable understandings of the biochemical

“cloud” constituting the cross-talk between parasites and their hosts.

2.2 Introduction.

Parasites are ubiquitous across ecosystems and often function as governors of their dynamics

[11]. These organisms have demonstrated their importance through a diversity of means, which

include providing balance to predator-prey dynamics and the subduction of colonization by

aggressive invaders [12]–[15]. The nature of a parasite is defined by the relationship that it exhibits

with its host(s); however, just as with other forms of life, it is the abiotic characteristics that prove

to define the biotic ones attributed to any given environment, and this remains true for the players

and interactions inherent of host-parasite relationships. It is these physical and chemical

8

pressures which dictate the coevolutionary histories and resulting predisposition for interactions

of species over time [16]; although, the abiotic facets of an environment are not the sole requisites

for the evolution of a parasite. In addition to demonstrating disparately different sizes,

achievement of candidacy for a parasitic relationship requires that the potential host and parasite-

organisms possess life history traits which facilitate their overlapping in space and time [17]. Each

organism demonstrates mutually-exclusive needs, and it is the overlap of a parasite’s requisites

with that of a host’s supply that further-qualifies a pairing for fostering of a parasitic relationship.

Explicitly, the fundamental niche of a parasite must include traits inherent to the body (or “biotic

landscape/microcosm”) of a host. For a host to fall within a parasite’s realized niche, however,

the biotic/abiotic pressures within the surrounding and host-attributed environments must also be

suitable in a spatial and temporal sense.

Host-parasite interactions are highly complex, layered systems complicated by the

underlying biochemical dynamics and reciprocating physiological systems at-play within

individuals as they interact over time. The resultant communication, or “cross-talk” back and forth

mediating these systems, is a ubiquitous trait of these relationships, acts as a driving force in

parasite evolution [18]–[21], and contributes to the sculpting of the parasite’s host-niche [22]–[24].

Parasite host-niche is defined by the interfaces of these interactions and the environmental

dynamics of host ecoregions within which they occur. This holds true for systems involving

entomopathogenic fungi, which display characteristic interfaces defining of particularly intricate

host-parasite associations.

2.2.1 In vitro cultivation of Entomopathogenic Fungi.

Lab cultivation of entomopathogenic fungi (EPF/EPFs) and its refinement has proven a popular

pursuit for a number of purposes. In addition to the optimization of spore production for biocontrol

applications in agricultural pest maintenance, these imperatives have also included the

improvement of fungal farming yields, whether those yields be fungal biomass, or for the

identification and production of high-value secondary metabolites, particularly in regard to

Hypocreales [25]–[31]. Most laboratory practices for the cultivation of EPFs prioritize contaminant

mitigation, but, in some cases, methodological adaptation for substratum-specialized species is

necessary to enable and facilitate growth. Typical practices consist of isolation, propagule

maintenance/inoculum preparation, quantification, and subsequent methods of infection or

bioassay [32]. Specific to Hypocrealean isolates, these practices anticipate certain requirements

and often reflect this in their tailoring and translation between varieties to avoid contamination or

loss of cultivars. For example, a lag phase is common for this fungal order when applying liquid-

9

fermented blastoconidia to solid substrata, predisposing these inocula to succession and

competitive exclusion; this period requires increased vigilance, for bacteria and other, more

competitive saprotrophic fungi, if given the chance, will more swiftly colonize solid substrata and

result in the loss of blastoconidia. Although many successful methods of cultivation have been

adapted, many more specialized varieties of Hypocrealean taxa have yet to be effectively isolated.

Common mycological practices, which are generalized for the cultivation of non-fastidious

species, have done little to promote the research of specialist fungi or to improve available

varieties of mycological culture medias better-enabling the study of such species. As a result,

media that would allow for reproducible, reliable propagation of these organisms in laboratory

conditions still remain undefined.

A fastidious microorganism is one that is unable to grow on traditional medias, requiring

highly-specific nutrients, growth factors, or an absence of certain environmental antagonists to

develop and replicate effectively [33], [34]. Conversely, non-fastidious microorganisms are often

noted for their insouciant style of growth, demonstrating varying degrees of adaptability to

relatively scant substrata. Select species within the order of Hypocreales have historically

exemplified more fastidious natures [35]–[37], and, considering the ecological context of these

fungi, this does not come as a surprise. Symbioses of any kind, particularly those demonstrating

expansive clade-permeant variability, have a tendency to complicate the process of

understanding an organism [38]–[41]. The order Hypocreales is particularly rich in species

demonstrating varying capacities for symbioses, and, of these, many also exhibit organismal

dualities as a function of host-context. Namely, these fungi have been shown to move between

roles as mutualists while within plant root systems to that of parasites with the introduction to an

invertebrate host-environment [41]–[45]. As a result of these complexities, the known or

suspected symbioses must be appropriately accounted for in the context of proper and

ecologically-relevant in vitro cultivation.

The expansion of EPF research, along with many other similarly-inhibited fields, was timed

with the development of artificial rich medias, many intended specifically for the culture of insect

tissues (e.g., Grace’s Insect Medium). Before this advent, it was routine to collect and pool insect

hemolymph for the constitution of a growth medium that would allow for successful cultivation of

these fungi in laboratory conditions [46]–[48]. Though these developments were crucial in

expansion of the field, the literature pertaining to EPF culture pales in comparison to those

dedicated to study of other parasite-host systems. This is especially true for the cultivation of

species that are known to be highly-specialized to their host [33], [34]. Although select recent

10

publications feature specialists as a facet of discussion, recommendations for in-lab specimen

maintenance only extend to traditional methods, passage and regular cultivation with use of the

host species in-question [32]. Further, the more numerous reviews concerning relevant

information are largely devoted to the resultant phenomena observed with continued use of rich

medias for EPF cultivation (i.e., attenuation of virulence) [46]. It is in the absence of extensive

information that imparts a need for greater effort be put-forth towards the curation and

organization of relevant research. The diversity in life history traits demands that care be applied

when establishing and maintaining viable laboratory specimens for the suitability, sustainability

and reproducibility of work with any one species.

A Study in Specialists: The Hemibiotrophic Fungus, Ophiocordyceps unilateralis s.l.

(Hypocreales: Ophiocordycipitaceae). Within the order of Hypocreales lay several families of fungi which are noted for their relationships

with various organisms, namely, those with plants [41], [49]–[53]. Hypocreales are designated as

Class 2 fungal endophytes, a class known for their abilities to confer habitat-adaptive traits to the

plants with which they are associated [54]. In addition to the order’s documented relationships

with plants, Hypocreales has also accrued a separate but equally-impressive volume of accounts

regarding its diversity of entomogenous7 species, which range from opportunistic to obligative,

and demonstrate varying degrees of symbiotic character (e.g., parasitic, commensal) [55], [56].

As one might subsequently expect, some of these insects have been noted for tendencies to build

and maintain very close relationships with plants [8], [55], [57]–[59]. This implied breadth of host-

range and anticipated host-jumping events have been substantiated through phylogenetic studies

of the fungi, their hosts, and even the viruses found to infect these fungi [60]–[62], [54].

The Hypocrealean family, Ophiocordycipitaceae, has been highlighted for its frequent

pathogenic associations with various insects and arachnids [63]. These associations seem to vary

in host-range as a function of their genus and species. Ophiocordyceps unilateralis s.l., however,

has demonstrated an impressive magnitude of host-specificity, the species of this complex being

known to infect only one species of ant each [64]–[66]. The life cycle of the species complex can

be generalized in five steps (Figure 2.0): 1) exposure of the host to an infectious propagule (i.e.,

ascospores); 2) a one- to two-week incubation period, during which time, fungal blastoconidia

replicate and disseminate throughout the host using the hemolymph; 3) climax of infection,

7 Entomogenous fungi (EGF/EGFs) are those which demonstrate a parasitic relationship with insects and can be assigned to one of three categories defined by the terms of the parasitism: entomogenous ectoparasite, entomogenous endoparasite, or entomopathogen. The former two being defined by an absence of host-termination, despite parasitism, either on the host’s exterior or interior, respectively [256].

11

resulting in the purported “manipulation” of the host for fixation to a surface within the respective

habitat’s canopy; 4) host death followed by hyphal growth and stroma production; 5) finally, after

full maturation of the ascus, ascospores are produced and dispersed over the forest floor below

to infect new hosts [67].

As discussed previously, specialists tend to require more selective medias for successful

cultivation, and this species complex is no exception to this. The host-specificity of this complex

has been observed in the field, and has also been tested under laboratory conditions. This trait

has been further-compounded by the implications of the system’s phylogenies and, even, by the

fossil record [68]. Further-supplementing this hypothesis, species within this complex have

demonstrated preference for particular ratios of carbon-to-nitrogen in artificial substrata, and also

have been shown to possess growth-contingency upon trace metal ions and macromineral salts

present in frequently-used rich medias [35]. Moreover, several species have exhibited drastic

reductions in virulence within only one or two platings/dilutions (unpublished data). Two, in

particular, Ophiocordyceps kimflemingae and O. camponoti-floridani, have exhibited markedly-

low growth rates in the culture medium of choice, PDA (Potato Dextrose Agar), taking

approximately three months to grow from a pinhead-sized tissue plug to that of a dime

(unpublished observations; within-lab correspondence). Subsequent production of blastoconidia

has been determined to be dependent upon the “freshness” of the sample and respective tissue

plug, a similar trait to what has been suggested by literature for other entomopathogens within

the family, Ophiocordycipitaceae; however, the virulence does not appear guaranteed with

successful cultivation.

2.2.2 Exometabolomics ‒ an ‘Omics’ Approach to “Hacking” the Host-parasite

“Cloud”.

Nutritional Immunity and Nutritional Ecology in Host-parasite Interactions. Endoparasites have been shown to demonstrate high-permeability, a trait coinciding with reduced

external defenses. This trade-off is thought to result in improved or facilitated exchange with their

host (e.g., nutritional uptake from the host). These parasites have evolved to optimize exchange

and nutritive curation from their surroundings, effectively minimizing the distance between

themselves and their hosts [69]; however, despite this reduction in distinction, they still maintain

themselves, as distinct individuals. With this, physical and chemical separation between the two

can always be anticipated, and, as a result, leveraged. The spaces occurring between hosts and

parasites can be utilized to improve the characterization of the biochemical environments

attributable to individual host-parasite relationships, in addition to their unique exchanges [70].

12

In evolutionary history, the increasing complexity of organismal development required

adaptations allowing for the accumulation and storage of nutrients [71]. These metabolites are of

high-value across the domains of life and can become a liability to a given host as these resources

are targeted by invading parasites [72]–[74]. Hosts have adopted many modes of response to

combat colonization by parasites, some of which result in the remodeling of the host landscape

and repositioning of its resources being targeted; this is frequently regarded as nutritional

immunity. These responses are used by organisms to prevent parasites from doing what they do

best: sequestering nutrients from their hosts. The maintenance of homeostasis and allocation of

energy sources can further-encumber a body fighting infection, making the balance of immune

system activity and physiological regulatory obligations quite unwieldy [75], [76]. In this way,

nutritional stores throughout host tissues can become instrumental pieces in a proverbial game

of chess, acting as scattered pawns to be won or lost by the parties involved [74], [77]–[79]. A

host’s innate and adaptive immunities work synergistically with nutritional immunity to mitigate

pathogen-sequestration of nutrients. An example of this synergism is the deactivation of

siderophores8 or inhibition of their production with the induction of hyperthermia [80]. Another is

the sequestration of trace ions by macrophages into digestive vacuoles, in effect, weaponizing a

contained but highly-oxidative, cytotoxic environment to kill endocytosed pathogens [81].

Nutritional immunity is an effective facet of organismal defenses; however, like many other

mechanisms of defense, pathogens have evolved ways to subvert it. Exemplary of this

phenomenon, Histoplasma capsulatum, a fungal pathogen of animals, requires endocytosis by

macrophages to effectively disseminate throughout and successfully colonize a host [82].

The mechanisms of nutritional immunity vary within and between species, and these

differences define the corresponding diversity of predispositions and susceptibilities of hosts to

various pathogens [70]. Nutritional immunity, like other organismal characteristics, demonstrates

plasticity between individuals, but also can be expected to reflect an organism’s respective

ecology [83]–[85]. Another field of study, nutritional ecology, addresses this concern, but has

seldom been employed to this end in host-pathogen interactions [71]. Reasons for this include,

but are not limited to, the difficulties in acquiring data allowing characterization of organismal

“cross-talk” at such a small scale, and these challenges are undoubtedly multiplied in the face of

host-parasite interactions. Fungi are particularly tightly-woven with regard to the relationships with

their hosts, and is a physical characteristic inherent to biotrophic and necrotrophic growth [86].

8 Siderophores are specialized proteins or molecular complexes produced by bacteria and fungi for the sequestration and transport of trace metal ions and macrominerals.

13

With such a small distance constituting the interstitial spaces between the host and parasite,

research examining the respective exchanges in the context of nutritional immunity and

ecophysiology require accurate, high-resolution methods of measurement and detection.

An ‘Omics’ Strategy for Subverting Scale and Complexity of Host-parasite

Interactions. Measuring the units of exchange within host-parasite systems has demonstrated a history of

challenges. As a result of their life history traits, many parasites have lost a number of genes

required for living independently from another organism, leading to physiological inflexibility and

their frequent description of being unculturable/uncultivable [34], [87], [88]. It is this obligate nature

of many parasites that has proved the primary governor of their difficult cultivation; however,

various ‘omics’ technologies have allowed for facilitated identification, isolation and study of these

finicky organisms. For example, Mycobacterium leprae, a causative pathogen of leprosy (also

called Hanson’s Disease) and obligate intracellular parasite, has only been identified through the

application of genomics techniques to tissue biopsies collected from deceased patients [89]. A

number of fields have been slowed as a result of possessing uncultivable microorganisms at their

centers, but the advent of ‘omics’ technologies has afforded opportunities for the capture of

biological “snapshots”, making these previously uncharacterizable forms of life “characterizable”

[90], [91]. With these technologies, scientists have been able to better understand the complex

symbioses facilitating coral fitness, and examine the extremophiles challenging the very definition

of what constitutes “life” [92]. They have even allowed us to expand the knowledge of our own

species, illuminating the microbial worlds on and within us that make us who we are [93].

Metabolomics9, compared to the technical development of other ‘omics’ fields, is still

somewhat in its infancy, but it is this method, in particular, that shows the most promise for

examining the molecular space definitive of host-parasite cross-talk. Endometabolomics, or

metabolomics techniques designed to observed intracellular molecules, has become a pivotal tool

in understanding the cellular biochemistry associated with cancers and their respective drug

treatments [94]. Exometabolomics, on the other hand, are methods which capture the biochemical

moment of the extracellular space—the metabolic “inputs” and “outputs” of a given organism [95].

9 Metabolomics ‒ “the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues or organisms. […] metabolites and their concentrations, unlike other "omics" measures, directly reflect the underlying biochemical activity and state of cells/tissues. Thus, metabolomics best represents the molecular phenotype.” Excerpt source: The European Bioinformatics Institute (EMBL-EBI)

14

2.2.3 Understanding a Parasite and its Host-niche through its Ecophysiological

Requisites.

The complex interplay of many biotic and abiotic environmental stresses and their influence gives

cause for their combined examination across many fields of study. Physiological ecology (also

called ecophysiology, or comparative physiology/environmental physiology) favors a more

mechanical approach and is the study of how living things function and adapt to various stresses

within their environments [6]. This field of study integrates measurements of the biotic and abiotic

pressures experienced by an organism, in place of more traditional, isolated biotic observations.

The inclusion of these diverse perspectives allows for examination of, both, the organism and the

environmental contexts from which it precipitated. An ecophysiological lens seeks to examine the

simple, recurring patterns underlying biological and ecological complexity; as a result, it lends

itself to the study of microbial communities and host-parasite interactions. Such complex systems

call for the implementation of improved technologies and techniques to meet the challenges they

present, and an ecophysiological lens may very well enable their application [87], [90], [96]–[98].

In effort to better understand the ecophysiological requirements, as well as putative host-

niche, of a fastidious fungal parasite, an exometabolomics approach was utilized to capture the

metabolic inputs and outputs of a known rich media. This technique offers an ideal steppingstone

for this experimental system, generating a path to more ecologically-relevant in vitro and in vivo

research. Although the ultimate, idealized objective will not be met by this research, it is the goal

of this project to lay the foundation for future work, as well as corroborate and supplement

previous molecular research of this host-parasite system. This collection of effort is intended to

facilitate improved standard operating procedures, experimental design, and reproducibility,

locally, as well as for others exploring bidirectional interactions of highly-specialized parasites

within their respective ecological contexts.

The problem examined within this work is that of developing an understanding of the

complex interactions demonstrated by a eukaryotic, hemibiotrophic parasite and its host, as well

as the many obstacles imposed by the nature of the relationship. In response, a focal question is

presented: how can we capitalize upon the “cross-talk” between these organisms to better

understand this relationship? To answer this question, an in vitro exometabolomics approach was

used to examine the putative exchange between organisms (Figure 2.0b). This strategy was then

used to develop a more ecologically-relevant selective medium for continued propagation in lieu

of within-host resolution.

15

2.3 Materials and Methods.

2.3.1 Liquid Fermentation of Blastoconidia for Longitudinal and End-point

Assays.

Inoculation and Incubation – Longitudinal Exometabolomics Studies.

Field samples were collected and topically sanitized using a bath of 79% ethanol prior to

dissection. Fungal tissue was then excised and placed onto a plate of fresh PDA containing

penicillin-streptomycin (100 ppm) and kanamycin (50 ppm). After development, the plug of fungal

tissue was used to inoculate each Erlenmeyer flask of liquid media (Grace’s Insect Medium,

Sigma Aldrich), which was subsequently sampled (330 µL/day, per sampling event) and extracted

in technical triplicate each day (100 µL per extraction; sample margin of 30 µL), over the course

of 21 days post-inoculation (dpi), including 0 dpi. All cultures were cultivated under low-/no-light

conditions at room temperature (~23 °C) and aerated at 130 rpm (VWR Incubating Orbital

Shaker). See 2.3.2 for exometabolomics assay protocol.

Inoculation and Incubation – End-point Exometabolomics Studies.

All assays subsequent of the two longitudinal studies were carried-out at end-point and were

inoculated with fresh blastospore solution, instead of a fungal tissue plug. For each series, the

same amount of spore solution was used from the identical spore source. Additionally, all cultures

were incubated under identical conditions as before, but with a difference in cultivation flasks

(tissue culture flasks, instead of Erlenmeyer flasks). To explore the physiological importance of

select trace metals and macromineral ions in blastoconidial growth and development, several

metrics were acquired at the end of a cultivation period of 11 dpi (12 days, total, including day

zero, 0 dpi). Measurements were taken after completion of the media sampling, sample-prep and

extraction exacted for exometabolomic analysis (technical replicate = 3; biological replicate =1).

Additional metrics included the differential pH (∆pH) and secreted protein (Ps) of the supernatant

for each incubated solution. All separate experimental batches cultured for these end-point

assays included an inoculated control flask and a non-inoculated control with the exception of one

set (for details and specific experimental series excluding a non-inoculated control, see

supplemental table, B2.0). For the minimal medias, in particular, each individual formulation

required pairing with an additional control (a non-inoculated flask containing each characteristic

media) for the duration of the cultivation period, in addition to their respective inoculated flasks

(experimental units).

16

Trace metal ions supplemented into growth medias were iron (Fe2+), copper (Cu2+), zinc

(Zn2+), and manganese (Mn2+). Macromineral ions chosen for supplementation were calcium

(Ca2+), potassium (K+) and magnesium (Mg2+). These cultivation experiments featuring

supplementation were performed in parallel with each ion’s chelation. The concentration of each,

respective supplement was determined through an examination of available literature as it regards

general fungal nutritional requirements [99]. For cultivation studies using a chelating agent, either

EDTA (Ethylenediaminetetraacetic acid) or TPEN (N,N,N′,N′-tetrakis(2-pyridinylmethyl)-1,2-

ethanediamine) were administered (again, see supplemental table B2.0 for experimental design,

concentrations and pairings). Additional cultivation series used in the development of minimal

media formulas and examination of morphology included three adjusted starting pHs (4.2, 5.0 and

6.6 pH), and asparagine- and methionine-fortified versions of the traditional rich media

(separately, each original concentration in Grace’s Insect Medium was effectively doubled). This

same set (biological replicate, n = 1) also included three additional flasks, each laced with

dibutyryl-cAMP, a signaling molecule-mimic, at three concentrations (1.0 mM, 1.5 mM and 1.7

mM). Upon minimal media formulation, which used data from the first longitudinal study of O.

kimflemingae and subsequent end-point assays of the same species, the medias were similarly

inoculated with blastospore solution and assayed at end-point (exometabolomics, differential pH,

and secreted protein).

2.3.2 Exometabolomics – Longitudinal and End-point Assays.

Sample Dilution and Methanolic Extraction.

To optimize the analytical balance of accuracy with sensitivity in the use of LC-MS

exometabolomic techniques, it was necessary to determine the linear range of the substrata used

across the experiments. This was ensured by performing a preliminary LC-MS analysis of the

substrata formulation, both, undilute and at four distinct magnitudes of dilution; the resultant

dilutions for comparison were as follows: 1:1, 1:2, 1:4, 1:8, and 1:32. With use of these results, it

was determined that a 1:4 dilution of the sampled media for the intended extractions would be

necessary to optimize peak resolution and sensitivity, effectively minimizing effects of ion-

suppression or peak-loss due to ions falling below the level of detection (data not shown).

All LC-MS samples were prepared in technical triplicate using a methanolic extraction. A

330uL sample of growth media was harvested from the culture and centrifuged 30 seconds at

max speed to remove cell debris. For extraction, 100 µL of supernatant was administered into

three 1.5 mL centrifuge tubes, each containing 900 µL of ice-cold methanol (100%, HPLC-grade,

Thermo Fisher Scientific). All extracted samples were placed in a -80 °C freezer for storage until

17

day of batch-injection for LC-MS analysis. Sample prep on the day of injection required an

additional pelleting step, transfer of supernatant to new 1.5 mL centrifuge tubes, and then drying

under a stream of ultra-pure nitrogen gas. These were subsequently resuspended in 400 µL of

HPLC-grade water (Sigma Aldrich) also containing an internal standard (1 µM Chlorpropamide).

All steps on the day of were performed on ice and with ice-chilled reagents. These were then

vortexed (vigorously, 2x 5 seconds each) and centrifuged at max speed and 4 °C for 10 minutes,

after which supernatants were transferred to new 1.5 mL centrifuge tubes. These were

subsequently used to contribute to the pooled quality control sample or QC (~100 µL, mass

spectrometry vial), in addition to their own, respective mass spectrometry vials (100 µL per vial

for each sample). All samples were run in randomized order using an Orbitrap Exactive Plus

(Thermo Fisher Scientific). QC samples were injected to constitute 5% of the total injected per

batch. Data acquisition was exacted through the injection of 10 µL per sample using a previously-

established method [100], with slight modifications (0 min = 0%B: 5 min = 20%B: 7.5 min = 55%B:

15 min = 65%B: 17.5 min = 95%B: 21min = 0%B; mass filters - 0-5 min = 85-800 m/z, 5-6 min =

100-800 m/z, 6-9.5 min = 85-800 m/z, 9.5- 15.5 min = 110 - 1000m/z, and 15.5-22.5 min = 250-

1000 m/z).

Feature Detection, Metabolomic Analyses and Data Visualization.

Thermo .RAW files were converted to .mzXML format using ReAdW (software). Targeted

analyses were performed using an open-source software [101] and a knowns list provided by the

laboratory of Dr. Manuel Llinás at Penn State University. Peaks were picked manually based upon

observed mass (+/-10 ppm, expected [M-H]-), peak shape, distance from expected retention time

(+/- 1 minute) and signal over background. Peak areas were exported into Excel for subsequent

analysis. All data were background subtracted using each analyte’s averaged method blank

value. Peak’s falling below averaged blank value or below 0 were imputed with a value of 10000

or average method blank value. These data were RSD-filtered (relative standard deviation, <

25%), and then log2-transformed relative to the averaged control values. Transformed data were

then used to generate figures (i.e., heatmaps, metaprints, metatracks, select analyte line plots).

The media components unable to be measured as a result of the analytical method were limited

to choline chloride and glycine.

To more closely compare the gross differences between species, a self-organizing maps

visualization technique was employed to differentially examine the metabolic signatures of each

in a holistic manner. This method, called suprahexagonal mapping, utilizes an unsupervised

learning algorithm (self-organizing maps, or SOMs) to allow for dimensional reduction of complex

18

data. This results in the generation of a 2-dimensional hexagonally-shaped plane composed of

smaller hexagonal cells reflecting their assigned, hyper-dimensional nodes. These cells are

organized in an ordinal fashion and bin-contents are assigned according to their relatedness in

data behavior, resulting in a highly-polarized visualization with impressive inferential power. The

provided training data and node behaviors are also used to generate subgroups or families of

behavior within the larger hexagonal plane called metaclusters and metacluster bases. With the

overlay of a new dataset for comparison to the training data, the topology—or cell contents (i.e.,

target compounds) and hyper-dimensional organizational structure across the 2-dimensional

plane—remain the same (i.e., metacluster bases and contained cell numbers); however, how

those cells are filled with visualization is a direct product of the overlaid data set. For longitudinal

hexagonal mapping, only, sets analyzed contained all possible metabolites of the targeted list; if

any metabolites failed to be detected in a given data set, zeros were inserted by default for those

observations, while all those detected maintained their respective data (RSD-filtered, log2-

transformed relative to the control average). For end-point hexagonal mapping, only shared

metabolite data were mapped. All enrichment analyses was performed through the MSEA

(metabolite set enrichment analysis) feature of the MetaboAnalyst web portal [102].

2.3.3 Assay of Secreted Protein.

For quantification of secreted protein, a small amount of supernatant was collected (~1-2 mL)

from each respective sample and administered into 1.5 mL centrifuge tubes. Samples were then

centrifuged for 20 minutes at 12,000 xg. Subsequently, these were filtered through a 0.45 µm

syringe filter using a 3 mL syringe (new syringe and filter were used with each sample). All were

injected into new 1.5 mL centrifuge tubes prior to colorimetric protein assay. For quantification of

secreted protein post-sample prep, a Pierce BCA Protein Assay kit (Thermo Fisher Scientific) was

used, as well as the included recommended method for a 96-well plate. As a result of determined

fit to the standard curve, samples were diluted to 10% and 14.29% their original concentration.

96-well plates were then scanned using a Biotek Synergy H1 Microplate Reader (absorbance,

562 nm). As a result of the limited number of technical replicates per sample, both concentrations

(10% and 14.29%) were used in the generation of figures in addition to their respective average.

Technical variance was exhibited in form of the standard deviation per sample and were applied

to the graphical representations of the averaged data (+/- calculated standard deviation).

2.3.4 Measurement of Supernatant pH and Differential Calculation.

For all pH measurements collected for the end-point studies and growth medium preparation

protocols, a traditional industrial-grade pH meter was used (Mettler Toledo™ S220

19

SevenCompact™ pH/Ion Benchtop Meter). After being centrifuged in 50 mL conical tubes for 10

minutes (15317 xg; Beckman Coulter Benchtop Centrifuge) at approximate room temperature (24

°C), supernatants were separated from the pelleted wet biomass and reallocated to new conical

tubes (two separate tubes with each sample) for pH measurement (biological replicate, n = 1;

technical replicate, n = 1). All pH measures were taken immediately with the termination of each

culture series designated for end-point assays (11 dpi). Differential pH (∆pH) was calculated using

starting pH and ending pH of each minimal media and used to generate figures. These calculated

values were corrected through subtraction of batch starting pH. For all other end-point studies,

differential pH (∆pH) was calculated using the pH of a non-inoculated control that was incubated

in-parallel under identical experimental conditions.

2.3.5 Figure Generation and Statistics of Exometabolomic Data.

Generation of all heatmaps and hexagonal maps (metaprints and metatracks) required use of the

supraHex package in R [103]. All color schemes used were customized (excerpt: basic <-

paste(c("midnightblue","white","darkred"), collapse="-")). Heatmaps were

produced using Euclidean distance and Ward linkage methods in clustering. Hexagonal mapping

was performed using default arguments with the exception of graphical parameters. All

enrichment analyses were either performed manually using common databases

(KEGG/HMDB/PubChem/ChEBI/MetaCyc) or with the use of the MetaboAnalyst web portal [102].

2.4 Results.

2.4.1 Longitudinal Exometabolomics of O. kimflemingae, in vitro.

To examine the physiological demands of Ophiocordyceps kimflemingae in the context of its

substrata, an extracellular metabolomics approached was implemented. Targeted metabolomics

was used to identify the changes in media composition over time as a function of fungal growth

in liquid culture. Surprisingly, O. kimflemingae demonstrated very little nutritional reliance upon

growth medium constituents (Table 2.0), although this media has been indicated as one of the

most commonly used media for in vitro cultivation of this and other species of Hypocrealean fungi

[35], [36], [104]. Initial examinations indicated both a temporally-defined metabolic shift mid-

course (~day 13) and, for a subset of analytes, distinct fluctuations over time (Fig. 2.1). These

findings were provided in further detail below, beginning with the examination of the gross

observations across analytes over time, and, subsequently, a review of each macronutrient

category and major findings.

20

2.4.2 High Productivity with Very Little Input: Lag Phase and Underlying

Compositional Dynamics of Growth Substrata.

In examination of temporal media dynamics with growth, characteristics between and across days

were analyzed to better understand the importance and putative interplay of metabolites relative

to fungal physiology. The features of the media over the course of this study suggested that this

parasite is impressively improvisational, and this was emphasized largely by the selective nature

of its nutritive uptake (Figure 2.1). The large majority of the analytes detected through targeted

analyses were demonstrated to have increased over time, while only a minority of those were

observed to have decreased in any substantial manner. Additionally, this organism demonstrated

a blatant lag phase prior to any indications of media utilization. The stasis appeared to dominate

the timeline until, on 13 dpi, marked changes across metabolites appeared to occur, this also

coinciding with the first observation of turbidity, a common indication of growth for cultivation in

liquid media (unpublished observations). This apparent shift observed with 13 dpi was quantified

by the quadrupling of more than 28% of detected metabolites between 12 and 13 dpi. A much

higher level of metabolic activity was suggested from this time point onward by continued media

compositional changes, and by the final observation of the study, 25 of 64 metabolites were

indicated as experiencing increased relative abundance over time. Conversely, and in additional

support of the organism’s resourcefulness, a minority of eight metabolites were determined to be

decreasing over the course of the experiment. These were examined as putative metabolic

“inputs”, and their complements, those which increased in the media, were examined as putative

metabolic “outputs”. The remaining 31 analytes were left to a third and final category which was

ascribed little to no change in relative abundance over time. Across all groups, enrichment

analysis was dominated by protein biosynthesis, ammonia recycling, urea cycle, malate-aspartate

shuttle and alanine metabolism network-association (Holm adjusted p < 0.05; see Allman et al.,

Antimicrobial Agents and Chemotherapy 2016). With the understanding that this organism

exhibited aggressive proliferation under experimental conditions, these associations indicated by

enrichment analysis did not come as a surprise.

2.4.3 Temporally and Magnitudinally Distinguished Metabolites.

Before examining the exometabolomic data on the basis of macronutritional class, analytes were

briefly highlighted based on having exhibited distinctive behaviors over the course of the

experiment. The largest absolute fold-change (average log2 fold-change per diem) was

21

demonstrated by orotate10, a metabolite not attributable to the growth medium formulation. In

magnitude, orotate was immediately followed by thiamine, ribose-5-S-homocysteine, asparagine,

tetrahydrobiopterin and ribose, respectively, of which three of five were contributors to the original

formulation. When direction of change is considered (i.e., increase or decrease relative to control

abundance), orotate maintained top rank, having experienced the greatest fold-change increase

in relative abundance throughout the course of the observations. Those which followed orotate in

magnitude of increase were ribose-5-S-homocysteine, tetrahydrobiopterin and indole-3-

carboxylic acid, respectively. Compounds exhibiting the largest log2 fold-change decrease were

thiamine, asparagine and ribose. The majority of metabolites within the increasing and decreasing

parental clusters exercised their namesake trends beginning upon 13 dpi with the exception of D-

gluconate, nicotinate, xanthine and hydroxyphenylacetic acid, all of which exhibited a much more

gradual increase long-prior to this time point (Figure 2.1). Metabolites experiencing non-

asymptotic or non-sigmoidal distributions across the full chronology of the experiment were limited

to glutathione, 2-oxo-4-methylthiobutanoate, tetrahydrobiopterin, N-acetylglutamate, xanthine,

hexose-phosphate, IMP, hydroxyphenylacetic acid and pyroglutamic acid. Pyroglutamic acid, in

particular, presented a unique case, as it is the time-dependent result of glutamate/glutamine

within each sample, pyrolyzing gradually across all samples over the course of the experiment

[105]. First, despite the expectation that this analyte would be correlated with the detected

abundance of glutamate/glutamine, it was not. In fact, observations actually seemed to indicate a

depletion of pyroglutamic acid subsequent of 13 dpi, in direct opposition with the time-dependent

accumulation across controls. Additionally, with pyroglutamic acid’s precursor, glutamate, having

demonstrated subtle increase through time, and, because of this, much was left unaccounted for

with respect to the details of this metabolite.

2.4.4 Characterization of Substratum-utilization Phenotype by Macronutrient

Category.

To determine the substratum-utilization phenotype of this organism, the exometabolomic data

were examined with the original media formulation as a guide for determining changes from the

media baseline compositional features. This facilitated reference to analytes that were available

in the media for uptake, which then permitted for simple discernment of differentially-removed or

-added components (Figure 2.1; Table 2.0). An initial overview of these results was provided, and,

subsequently, was further reviewed in more detail for each respective macronutrient category.

10 Orotate is an organic acid commonly produced as a result of the pentose phosphate pathway from 5-Phosphoribosyl diphosphate; orotate then feeds into pyrimidine and purine metabolism.

22

According to the resulting data, O. kimflemingae demonstrated a high level of nutritional

selectivity. This was proffered by the fact that only 8 components out of 36 detectable formulation

analytes indicated noticeable decrease and were clustered as such (contents of decreased-

detection parental cluster). Metabolites within the media detected as being depleted or decreased

were observed to include two vitamins (thiamine and pyridoxine), three amino acids (asparagine,

cystine and methionine), two carbohydrates (sucrose and ribose) and one atypical amino acid

(pyroglutamic acid). Thiamine and asparagine were the most aggressively removed from the

media, exhibiting log2-fold change slopes of -13.69 and -5.96 between 11 and 13 dpi, respectively.

Conversely, the amino acid, methionine, and amino acid dimer, cystine, were of a somewhat more

gradual decrease and demonstrated a small time-delay compared to the initial depletion of

thiamine and asparagine. The removal of pyridoxine was less pronounced compared to thiamine,

while the depletion of detected carbon sources, sucrose and ribose, were understood to have

been swift, although differing in the time-point at which they surpassed below their respective

limits of detection.

While there were several blatant characteristics within the data, other, subtler, trends were

also observed. The only other amino acids demonstrating any level of depletion that were also

featured in the media formulation were alanine, isoleucine, leucine and phenylalanine. Alanine

and isoleucine only showing minor removal beginning with 14 dpi, whereas the latter two exhibited

low rates of removal on and after 19 dpi. Other amino acids, vitamins and carbon sources present

in the growth medium formulation were not determined to have been removed to any notable

degree. Interestingly, some of these were, instead, increased in detection co-temporally with the

aforementioned indications of media utilization. Some of those exhibiting this behavior showed a

unique pattern of decrease subsequent to this increase over time. The five amino acids that

exhibited this characteristic behavior were proline, valine, β-alanine, serine and threonine.

Carbon Sources and Contributors to Core Carbon Metabolism ‒ Sucrose and Ribose.

Carbohydrates were the largest contributors by mass to the formulation of Grace’s Insect Medium

(Table 2.0). There were three sugars featured in the medium formulation: two hexoses (i.e.,

glucose and fructose), and one disaccharide (i.e., sucrose, a nonreducing heteromeric dimer

composed of one glucose and one fructose molecule). Sucrose was, by far, in the greatest supply,

contributing more than half of total ingredient mass and approximately 92% of the mass of total

primary carbon sources. An additional sugar, a pentose (e.g., ribose) was also detected as being

present in the media, not by formulation, and was indicated as also having been removed over

the course of the study. Organic acids central to carbon metabolism were other relevant

23

components featured in the medium formulation; these included: fumarate, succinate, malate and

α-ketoglutarate (i.e., each of these four are known to directly contribute to the TCA cycle). Each,

carbon sources and other contributors to core carbon metabolism, were used below to

categorically examine carbon-acquisition as indicated by the longitudinal exometabolomic data of

O. kimflemingae.

The selective reliance upon starch-derived sugar monomers and their required,

corresponding exoenzymes have been well-documented as a fundamental basis of fungal

nutrition [106]. Previously, glucose has been observed as the preferred carbon source of a closely

related fungus within the same species complex, Ophiocordyceps unilateralis (BCC 1869), when

grown in liquid culture [35]. Initial review of the data suggested a very similar carbohydrate

preference, as sucrose, which contains glucose, was one of the few analytes determined as

having been removed from the media in any substantial manner. Upon further investigation,

however, results implied that the dimer, sucrose, was being broken down prior to differential

absorption of the hexose monomers. Interestingly, O. kimflemingae appeared to refrain from

removing simple hexoses, and, instead, seemed to slowly remove a related product, hexose-

phosphate. In addition to the increased-detection and subsequent persistence of simple hexoses

in the media, a preferential removal of the detected 5-carbon sugar was observed to have been

completely removed (Figure 2.2a). Further, with the depletion of sucrose from the media (4.91

log2-fold decrease), hexose was shown to increase substantially relative to control values with

and subsequently to 13 dpi (4.71 log2-fold increase). Simultaneously, ribose is shown to begin a

graded decrease, finally experiencing a sudden drop-off with 19 dpi (average -9.98 log2-fold); the

detected relative abundance of ribose dropped to 11.51 log2-fold. Hexose-phosphate, a

phosphorylated form of hexose, was determined to have increased sharply in the media with 14

dpi, peaking in the media on 16 dpi (9.82 log2-fold increase), before decreasing over the remaining

5 days (average slope -0.33 log2-fold difference per dpi relative to control).

As central contributors and intermediates of core carbon metabolism, organic acids were

also examined in greater detail, and, with this and the resultant metabolic footprint of this

organism, were determined as likely ambient indicators of metabolic activity as it involves shunts

to, from, and through various legs of the TCA cycle. As suggested per this warrant, several organic

acids proved insightful in examination of the putative metabolic form and function indicated by the

exometabolomic analysis of O. kimflemingae over the course of this study (Figure 2.2b). Fumarate

began a gradual increase on 13 dpi, eventually culminating to 0.79 log2-fold in abundance relative

to control values. Similarly, succinate, a downstream TCA intermediate which results from the

24

oxidation of fumarate, also demonstrated a gradual increase initiated with 13 dpi and ultimately

exhibited a relative-increased abundance of 0.64 log2-fold by 21 dpi. Interestingly, the hydrated

product of fumarate as mediated by fumarate hydratase (or fumarase), malate, did not differ

substantially from control levels at any point over the course of the experiment. On the other hand,

α-ketoglutarate — the organic acid that precedes succinyl-CoA and, therein, succinate, within the

TCA cycle — began demonstrating a sharp decrease in detected relative abundance upon 15 dpi

(-2.27-log2 fold from 14 dpi), reaching a trough on 19 dpi.

Nitrogen Sources ‒ Asparagine, Atypical and Sulfur-containing Amino Acids.

Across organisms, amino acids are highly valued for their nitrogen-containing moieties; however,

in the instance of fungi and other microbes, they can also serve as a crucial source of sulfur. As

per respective exometabolomic data, O. kimflemingae has been indicated as no exception to this

mode of acquisition. What’s more, this Hypocrealean species was indicated as removing an

atypical amino acid, pyroglutamic acid, from the media, which suggested the rather characteristic

ability to utilize this irregular nitrogen source, a seldom reported capacity that has been

documented in select species of fungi [107], [108]. These two exceptional qualities demonstrated

by O. kimflemingae’s metabolic footprint were examined in further detail, below, as they relate to

the corresponding organism’s physiology and nutritional requirements.

The metabolic footprint indicated that the select few putative nitrogen sources utilized by

O. kimflemingae also happen to involve the two, sole amino acids which can also serve as organic

sources of sulfur: methionine and cysteine. Two of the three most-swiftly depleted nitrogen

sources found in the original medium formulation, cystine11 and methionine, contain this trace

essential element, although in very different forms (Figure 2.2c). Methionine contains sulfur within

its S-methyl thioester side chain. Cystine, on the other hand, is constituted by two cysteine

molecules — cysteine being the only amino acid to contain a sulfhydryl moiety — that are joined

by a disulfide bridge. The separation of the two cysteines is reported to occur readily in the

presence of mild reducing agents; however, exoenzymes evolved for the specialized hydrolysis

of these linking moieties have also been documented in fungal species [109]. Here, cystine was

shown to decrease by 4.45 log2-fold following 13 dpi. Cysteine, in contrast, demonstrated a minor

decrease (1.38 log2-fold) between 12 dpi and 14 dpi, a trend that was markedly interrupted with

the subsequent time point, 15 dpi, where the analyte initiated a steep increase in relative

abundance of 1.83 log2-fold. This trend continued for only one additional time point, 16 dpi, the

11 Cystine is commonly used in artificial mediums and supplements as a stable source of the amino acid cysteine, of which it is the dimeric form.

25

value of which was thenceforth reiterated across the remaining observations generating a plateau

of approximately 1.49 log2-fold relative to control values. Methionine, in contrast to cysteine or its

dimerized form, exhibits a clean, gradual depletion beginning on 13 dpi and amounting to a final

decrease of 4.39 log2-fold from initial relative abundance. Asparagine is a proteinogenic amino

acid containing a carboxamide side chain and α-carboxylic acid moiety, characteristics conferring

much of its value as a pivotal and highly-flexible intermediate between many core metabolic

processes (Figure 2.2c). In this study, asparagine distinguished itself from all other amino acids

removed from the media, being depleted below accurate detection levels within only 48 hours.

More explicitly, asparagine was determined to decrease by 14 log2-fold relative to control levels

between 12 and 14 dpi.

The exometabolomic data of O. kimflemingae suggested an ability to remove an atypical

amino acid from the media, a capacity known to be a relatively uncommon or lesser-characterized

phenotype among eukaryotes. Pyroglutamic acid, although not a formal contributor to the growth

medium formulation, is a naturally occurring lactam-form of glutamate or glutamine, two amino

acids which do contribute to the medium’s known composition (Table 2.0; Figure 2.2c). Despite

the demonstrated time-dependent accumulation of this cyclized-form across control samples, the

relative abundance of pyroglutamic acid was sharply reversed beginning with 15 dpi (5.67 log2-

fold decrease from 14 dpi) for the inoculated samples, ultimately reaching a trough on 17 dpi of

5.44 log2-fold decrease. It is this demonstration of continuous accumulation in the absence of this

fungus which further implicated the posited faculty for utilization of the atypical amino acid.

Vitamins ‒ Thiamine and Pyridoxine.

As essential vitamins, thiamine and pyridoxine are required cofactors for many catabolic, anabolic

and anaplerotic reactions. Many of these include requirements in-conjunction with

decarboxylases, mediating the degradation of amino acids into catabolites that function as

precursors for purine and pyrimidine synthesis, as well as many secondary metabolites. In this

exometabolomic study, O. kimflemingae demonstrated selective auxoautotrophism12 and

auxoheterotrophism13 for various vitamins. Of those measured, thiamine and pyridoxine were

indicated as the only vitamins for which the fungus was auxoheterotrophic. This was suggested

by the fact that only these two vitamins were observed to decrease in relative abundance over

time within the metabolic footprint. The depletion of thiamine was the most severe, exhibiting a

12 Auxoautotrophic is used to describe an organism with the ability to produce its own supply of a select essential nutrient. 13 Auxoheterotrophic is used to describe an organism that is deficient in an essential nutrient, as it is unable to synthesize it itself.

26

13.69 log2-fold decrease within 24 hours (from 12 dpi to 13 dpi) (Figure 2.2e). Pyridoxine, on the

other hand, was removed in a less-abrupt fashion, demonstrating a steep decline subsequent of

13 dpi, of which, thenceforth, decreased in severity in-approach to 19 dpi (Figure 2.2f). The overall

decrease of pyridoxine from the initially detected abundance was approximately 5.38 log2-fold

relative to control values. Folate, another vitamin detected, appeared to exhibit behavior indicating

that it was relatively stable around the feature’s limit of detection, showing no substantial decrease

or increase relative to control values.

O. kimflemingae was shown to be auxoautotrophic for select, detectable vitamins

contained within the formulation (Table 2.0), and therefore was determined to not require their

provision via substrata. Indicated by a notable increase in detection during the observation period

relative to control values, these vitamins were limited to pantothenate (9.40 log2-fold) and

nicotinate (3.88 log2-fold change).14 Pantothenate began a noticeable increase with 13 dpi.

Nicotinate also showed relative-increased detection (Figure 2.2f). Initial accumulation of the

vitamin began with 13 dpi and culminated to a height of 3.88 log2-fold difference relative to control.

The observations indicating auxoautotrophically-derived pantothenate accumulation suggested

that the affiliated source for substratum-dependence lay upstream of the vitamin, biosynthetically.

To better understand the sources from which O. kimflemingae was potentially synthesizing this

vitamin, the known precursors of pantothenate, alanine and β-alanine, were visually juxtaposed

with their anabolic successor (Figure 2.2e). In this way, it was determined that the formulation

component most-likely being used for pantothenate biosynthesis by this fungus was alanine. This

was deduced from the additional auxoautotrophism demonstrated for β-alanine, which

corresponded with the depletion of alanine and the intermediate of the two, aspartate, from the

media.

2.4.5 Follow-up: Supplementation of Ionic Cofactors, Chelation and

Phenotype Rescue.

Many instrumental cofactors required by metabolic pathways are present in substrata as free

ions15, and can act by activating or catalyzing enzymatic reactions. Alternatively, they can work

in-concert with other cofactors16 to facilitate biochemical processes and are often vitamin-derived

14 Although included within the raw data, p-aminobenzoate and myo-inositol did not pass the RSD-filtering process. 15 Cofactors can be described as being either inorganic (e.g., metal ions; iron-sulfur clusters within cysteinyl residues) or organic (e.g., vitamins and derivatives; non-vitamin-derived metabolites; protein-derived; non-protein/off-target participants). 16 In this case, synonymous with “coenzymes”; coenzymes are frequently sub-classified as either cosubstrates or prosthetic groups, which are either transiently- or permanently bound to an enzymatic protein within a complex, respectively.

27

coenzymes or are synthesized at low concentrations from other essential nutrients (e.g., NAD or

tetrahydrobiopterin, respectively). Many layers constitute the pathways within organismal

metabolisms and cofactors function as the gatekeepers and regulators of these fundamental

processes. Incorporation of these trace nutrients is crucial for developing a complete “chemical

snapshot” of any organism’s physiology.

To determine this more complete “chemical snapshot”, these inorganic cofactors, such as

zinc and copper, were selectively supplemented through administration or removed through

chelation, and their metabolic signatures compared. Blastospores inoculated into rich media were

cultivated for a fixed period and assayed at end-point for generation of their respective metabolic

footprints (Figure 2.3a; Figure 2.3c; supplemental table B2.0)17,18. The footprints of these series

exhibited similarities within and between culture sets, all of which were performed in identical

experimental fashion. Suprahexagonal visualization allowed for emphasis of those which were

distinct within and across each set. The most visually distinguished of the signatures were those

resembling the non-inoculated samples within their respective series, a status which could,

generally, be considered the closest signature to one indicating “dead” or “no growth”, from an

exometabolic standpoint. At physiologically-relevant concentrations, zinc, calcium and copper

were indicated as being capable of reenabling growth in the presence of TPEN at equimolar

concentrations, whereas manganese, although successful in the instance of EDTA, was unable

to rescue blastoconidia proliferation. Conversely, zinc, which was successful in rescuing growth

in the presence of TPEN, failed to do so in the presence of EDTA; however, the capacity of zinc

to restore wild type growth in the latter case was not done so at a physiologically-relevant

concentration. Only copper proved to rescue in both cases (i.e., equimolar concentrations of

TPEN or EDTA). Potassium and magnesium failed to rescue under the regime of either chelator,

and, although iron also failed in the case of TPEN, it was not tested in the presence of EDTA.

While these studies only began to explore their necessity, it is clear that trace ions play a key role

in parasite physiology and will require more careful examination in the future to determine their

true importance.

17 For hexagonal map base-, hits-, distribution-, metacluster-, and index topologies, see supplemental figures B2.2a-e. 18 For heatmaps corresponding to the individual data sets per assay type (supplementation, chelation/titration, or rescues), in addition to each condition type’s differential pH and relative secreted protein, see supplemental figures B2.3(a-i).

28

2.4.6 Comparison of Two Hypocrealean Species Using Longitudinal

Exometabolomics.

Interspecific Comparisons with the Use of Self-Organizing Maps. To examine how this exometabolomic phenotype might differ between species, an additional

longitudinal study was performed for a second variety within the same species complex as O.

kimflemingae, Ophiocordyceps camponoti-floridani. These data over time and the respective

generated exometabolomic signatures were compared between this and the prior-studied species

(Figure 2.4; supplemental figures B2.5a-e; supplemental table B2.6). The resulting figures

provided readily-comparable graphics or so-called “metaprints”19 [110] representing the profiles

of each species under identical cultivation conditions for 21 dpi, of which can be examined as

paired sets of longitudinal metaprints or “metatracks” (Figure 2.6). Differences were superficially

discernable and exemplified by the large red core of features that resulted in the overlaying of

data for O. camponoti-floridani. It was also clear that the gross behavior of O. camponoti-floridani

was more diffuse or evenly-distributed across the hexagonal plane, which contrasted with the

stark polarization observed with O. kimflemingae (Figure 2.6). It was evident that the detected

metabolic activity of O. kimflemingae (or OKf in tables/figures) had been offset by a delay, or lag-

phase, of 12 dpi when compared to O. camponoti-floridani (or OCf). The mechanism mediating

this difference was not plainly apparent. For the proper comparison of macronutrient uptake and

putative secretomes, the study termination date and the point at which both organisms could be

considered metabolically mature, 21 dpi, was used as the time-point of reference (Figure 2.7a).

The metaprints of the two species were compared side-by-side to determine metabolites of

interest, and were topically compared by the macronutritional subgroups discussed prior. With

input of the training data (O. kimflemingae), the hexagonal plane was organized into four (4) major

metacluster base regions (supplemental figures B2.5a-e; supplemental table B2.6). To further

parse possible differences in phenotype and putative host-niche, exometabolomic data for each

species was examined on a single-metabolite level using cell-assignments as the primary guide

for discussion. Differences in analytes determined to be putatively secreted by each organism

were also examined in accordance to their relationships to the metabolic inputs indicated.

Carbon Sources and Contributors to Core Carbon Metabolism ‒ Sucrose and Ribose.

O. camponoti-floridani demonstrated wider coverage of carbon sources supplied by the medium

formulation, however, all the same, indicated no overlap of sugar preference with the other

19 Metaprinting is a useful tool for visualizing and comparing metabolomic data; the method utilizes an R package, supraHex, which employs self-organizing maps to create a 2-D representation of data features competitively-assimilated by their learned categories of behavior.

29

species. Contrasting with O. kimflemingae, which was not observed to utilize the detected simple

hexose sugars, the opposite was observed in O. camponoti-floridani. Ribose and hexose, each,

demonstrated relative-decreased detection in the media over time (Figure 2.7a). Interestingly,

sucrose, the putative sugar source for O. kimflemingae, the exometabolomic signature of O.

camponoti-floridani did not exhibit any hint of removal from the media. On the other hand, hexose-

phosphate was shown to increase (node 61, Figure 2.6), and in a manner very similar to that

observed in O. kimflemingae; of cautionary note, in case of the latter, it was observed to

subsequently decrease over the remaining days, differing plainly from the plateau demonstrated

by hexose-phosphate for O. camponoti-floridani.

Amino Acids.

Similarly to what was observed in the case of O. kimflemingae, select amino acids were prioritized

over others for removal from the growth media of O. camponoti-floridani. Preferences for

asparagine, methionine and cystine were, again, demonstrated (Figure 2.7b). The media of O.

camponoti-floridani was distinguished from that of O. kimflemingae by the depletion of a much

broader range of amino acids. This was in addition to a marked removal of the amino acid

monomer, cysteine, with that of its dimer, cystine, which, in stark contrast, saw a sudden increase

and plateau with no subsequent removal from the media of O. kimflemingae. Although slightly

more graded at first, O. camponoti-floridani exhibited an abrupt removal of asparagine (node 77,

Figure 2.6) compared to that observed with O. kimflemingae. Methionine (node 80, Figure 2.6)

demonstrated a very gradual, almost linear, removal in the case of O. camponoti-floridani, which

contrasted with its swift removal observed with O. kimflemingae. Cystine, an analyte co-localized

with methionine, shows similar behavior in that the feature exhibits substantial decrease for each

species, but the pair is differentiated from cystine’s monomer, cysteine (node 18, Figure 2.6),

which showed increase in the case of O. kimflemingae. Interesting, and further emphasized

through the paired hexagonal maps, O. camponoti-floridani demonstrates nearly identical

behavior for cystine and cysteine, appearing to remove the analytes completely from the media.

The removal of alanine, which was observed in O. kimflemingae, was a behavior swapped with

that of beta-alanine in the case of O. camponoti-floridani. Interestingly, pyroglutamic acid (node

51, Figure 2.6) was utilized at a much less-impressive rate in O. camponoti-floridani compared to

that which was observed in O. kimflemingae; in fact, its removal from the media for O. camponoti-

floridani was only detectable when the time-dependent accumulation of the amino acid was

accounted for, which was, again, facilitated by the hexagonally-mapped data. One of the benefits

of the using metaprints, the emergence of cryptic players, was made readily apparent with these

30

side-by-side comparisons. Specifically, it resulted in the emphasis of certain amino acid groups

over others, such as branch-chain (distributed; nodes 30, 73, and 33, Figure 2.6) and cyclic

(adjacent; nodes 71, 72 and 73, Figure 2.6) amino acids present in the media, as well as those

which are known to directly contribute to the TCA cycle (nodes 17, 55, 83, and 56, Figure 2.6). It

also highlighted catabolites and anabolites downstream of those components of the media

indicated as putative metabolic “inputs”. These downstream analytes, for example, included

aconitate and glutathione (node 90, Figure 2.6). Both species, despite their blatant distinction in

amino acid uptake, were indicated as putatively secreting catabolites of indoles, or, more-

specifically, tryptophan. These metabolic byproducts included indole-3-carboxylic acid (ICA) and

kynurenic acid (nodes 63 and 38, respectively).

Vitamins ‒ Thiamine and Pyridoxine.

As exhibited by O. kimflemingae, a pointed removal of select vitamins was a trait also

demonstrated by O. camponoti-floridani over time. Thiamine, in particular, was again removed in

a similarly aggressive manner to that of the first species (node 77, Figure 2.6). Pyridoxine, another

vitamin removed in the presence of O. kimflemingae, was also depleted over time. However, its

removal from the growth medium of O. camponoti-floridani was much more gradual and less

severe overall relative to control values (supplemental table, B2.6; supplemental figure, B2.4).

Nicotinate and pantothenate both demonstrated increase within the medias of the two species

(node 61, Figure 2.6), the two being differentiated only by the initial lag phase demonstrated in O.

kimflemingae.20

2.4.7 Ophiocordyceps kimflemingae – Extracellular Physical and Chemical

Perturbation with Objective of Minimal Media Development.

Many organisms readily colonize and exploit basal or rich medias, making their use in isolation

protocols difficult and often leading to the amplification of any competition-selected species

present within a given sample. To combat this problem, antibiotics are often used in this context,

which have demonstrated the capacity to alter and even inhibit normal growth of blastoconidia in

vitro (unpublished data). Conversely, minimal selective medias are commonly developed for

improving the experimental evaluation of organisms grown under lab conditions, detection of

auxotrophic mutants or similar microbial strain maintenance. These simpler medias may also be

supplemented with select ingredients for differential or selective cultivation of secondary

metabolites. The value of minimal medias in the cultivation and experimental testing of microbes

20 Biotin failed to pass RSD-filtering for O. camponoti-floridani, and was therefore held from being used in the comparative analysis.

31

is quite common-place and, for the length of microbiology’s history as a field, has been held as a

reliable material strategy in labs requiring microbes or microbial cultivation for research.

For example, the so-described “fastidious” fungal genus, Hirsutella, the former

anamorphic genus of Ophiocordycipitaceae, now phylogenetically-merged, has been thoroughly

studied in interest of overcoming the challenges that often deter the development of high-

throughput cultivation strategies in order to foster the fungi’s potential as a biocontrol agent in

agricultural systems [37], [111], [112]. Further, the use of such medias in the cultivation of more

fastidious organisms often can prove tedious and unreliable due to high rates of contamination

and inappropriate nutritional content, many of which have been shown to actually inhibit growth

(e.g., by acting as non-specific antagonists/agonists of membrane transporter proteins or as

growth-deterring transcription factors), cause cytotoxicity, or cell death [113]. The physiological

phases of a fungi’s life cycle are directly tied to the status and constitutional matrix of its substrata

for successful colonization, sustenance and subsequent dispersal of propagules. It is also this

media specificity that governs the diversity of microbial communities coexisting with fungi and the

characteristics of microbial succession which take place over time as the extracellular matrix

continues to degrade and change.

In follow-up to previous studies, analytes from prior exometabolomics analyses

(longitudinal exometabolomic study of O. kimflemingae) were chosen to be doubled in

concentration in substrata for examination of their nutritional importance and to, therein,

determine the limiting nutrient of the two: asparagine and methionine. Subsequently, select abiotic

factors were also tested to elucidate their possible influence upon fungal physiology and resulting

emergent substratum-utilization phenotype, the profiles of each generated through use of end-

point exometabolomic assays. These abiotic factors included three incremented concentrations

of a cyclic signaling molecule-mimic often used in the study of various eukaryotic organisms (i.e.,

1.0 mM, 1.5 mM and 1.7 mM dibutyryl-cAMP) and three distinct starting pHs (i.e., 4.2, 5.0 and 6.6

pH). Below, these adjustments to the growth medium and their respective impacts upon the

substratum-utilization phenotype of O. kimflemingae were described. Finally, to test the

cumulative knowledge regarding the baseline nutritional requirements of O. kimflemingae, a final

series of minimal medias were formulated, prepared, and examined for viability through a final set

of end-point assays and determination of metabolic footprints in vitro. These profiles were then

compared to that which was demonstrated by the same blastoconidia cultivated using the original,

rich medium formulation of Grace’s Insect Medium in-parallel under identical experimental

conditions.

32

Physical and Chemical Perturbation.

Examining Surpluses of Asparagine and Methionine.

To examine the viability of the nutritional requisites implicated by the exometabolomic longitudinal

data of O. kimflemingae, asparagine and methionine were selected for follow-up experimentation.

Because individual components necessary for the preparation and implementation of drop-out

medias were not readily available, a converse, fortification-defined approach was applied to

examine whether these nutrients could reflect their significance in promoting fungal growth

through a surplus of these select nutrients. To suit this objective, asparagine and methionine were

added in-excess to the standard rich media (Grace’s Insect Medium). Relative to the original

medium formulation, these amino acids were effectively doubled in concentration as a result of

fortification. This was performed separately in-culture for each nutrient and compared to that of a

parallel-cultivated inoculated control (a technical replicate for the media formulation was also

used). For this and two other distinct media supplementation trials, exometabolomic analyses

were performed after 11 dpi, as well as measures of differential pH and total secreted protein.

Morphology was also examined for qualitative comparisons of treatments.

For both species examined through time-series exometabolomics, methionine and

asparagine were demonstrated as being swiftly removed from the original media formulations

over time. End-point exometabolomic signatures for two selectively-fortified formulations were

visualized as peak area values that were log2-transformed relative to the inoculated control data

(Figure 2.8). Gross differences between the two conditions were reviewed by their hierarchical

clustering and, thenceforth, were utilized for group-wise enrichment analysis. MSEA of first of four

parental clusters, containing a total of 8 analytes, indicated association with aspartate

metabolism, β-alanine metabolism, glycerolipid metabolism and glycolysis (p-value = 8.02E-5, p-

value = 0.00462, p-value = 0.00462, and p-value = 0.012, respectively). The general behavior of

this cluster was that of relative-increase for asparagine-fortified condition, and relatively no

change in the case of the methionine-fortified condition. The second parental cluster contained a

total of 38 analytes, and MSEA of the cluster indicated association with protein biosynthesis,

ammonia recycling, the urea cycle and glycine/serine/threonine metabolism (p-value = 5.89E-9,

p-value = 0.0044, p-value = 0.00657, and p-value = 0.0171, respectively). Changes observed

across the analytes of this group were not of noticeable character. The third of four parental

clusters only contained 3 analytes, making enrichment analysis impractical. Those analytes

included 2-oxo-4-methylthiobutanoate, uridine, and methionine. Despite the few analytes

designated in this cluster, a trend was plainly apparent, exhibiting relative increase for the

33

methionine-fortified condition and little to no change for that of the asparagine-fortified condition.

The fourth parental cluster—which demonstrated relative decrease across analytes for the

asparagine-fortified condition, and relatively no change with some instances of relative-increase/-

decrease in the context of the methionine-fortified condition—contained 20 analytes and showed

enrichment for phenylalanine/tyrosine metabolism, protein biosynthesis, tyrosine metabolism and

catecholamine biosynthesis (p-value = 1.01E-4, p-value = 0.00682, p-value = 0.0457, p-value =

0.105, respectively).

Differential pH (∆pH) for both of these experimental conditions were determined to be

equivalent (a decrease of 0.02 from control value; supplemental figure, B2.7a), and roughly

unaltered relative to the control at end-point. The methionine-fortified condition exhibited a lower

combined mean secreted protein relative to the series’ respective control (-0.05 µg/mL; combined

mean being derived from the 10 and 14% sample concentrations), while the asparagine-fortified

condition, in contrast, demonstrated increased secreted protein relative to control value (+0.05

µg/mL; supplemental figure, B2.7b). In regard to brief morphological comparisons between all

extracellular perturbation conditions, the doubling of asparagine in the media produced the most

distinctive change compared to that of other regimes and control morphology (supplemental

figure, B2.8). The resultant phenotype of this condition, in particular, presented as exceptionally-

compact spherules occurring at much higher densities than those observed as a result of other

conditions. Nevertheless, additional research will be necessary to determine which of these two

nutrients are truly limiting.

Introduction of a Signaling Molecule ‒ Dibutyryl Cyclic-AMP.

Cyclic nucleotides and similar signaling molecules have long-been linked to fungal

morphogenesis, growth and development [114]–[117]. Fungal pathogenesis as it regards plant

and animal pathogens has also been associated with these small molecules, which have been

observed acting as key factors orchestrating both virulence and host-colonization [118], [119].

These modes of regulation for fungal pathogens appear to not be limited to those which infect

endothermic or vertebrate hosts; entomopathogenic fungi also have been documented as utilizing

similar molecules, like cyclic adenosine monophosphate (cAMP) or cyclic guanosine

monophosphate (cGMP), to regulate their physiology as they infiltrate, colonize and interact with

their hosts [120]–[122]. Dimorphism and the nature of dimorphic transitions in fungal pathogens

have been characterized for various species and have been observed to be mediated by cyclic

nucleotides via MAPK and cAMP-PKA signaling pathways [114], [123].

34

As with previous trials of extracellular perturbation, blastoconidia were cultivated in liquid

culture for 11 dpi prior to performance of end-point bioassays examining growth media

composition and biochemical alterations relative to controls (i.e., exometabolomic signature

generation, differential pH, and secreted protein). Prior to inoculation with blastoconidia, three

distinct concentrations of dibutyryl cyclic adenine monophosphate (dibutyryl-cAMP) were

administered to separate tissue culture flasks containing growth media. The experimental

concentrations were determined through reference of relevant literature21. These three

concentrations mimicked similar work in Mucor rouxii as it applies to the newly introduced analogs

described by Pereyra et al. (Microbiology, 2000). Specifically, the three concentrations employed,

here, were 1.0 mM, 1.5 mM and ~1.7 mM dibutyryl-cAMP.

Targeted exometabolomic analyses of the three aforementioned experimental culture

conditions and subsequent heatmap generation resulted in four primary clusters reflecting the

behavior of detected analytes upon data analysis and subsequent visualization (Figure 2.9).

MSEA of the most-vertical of the four parental clusters indicated analyte-collective associations

with aspartate and pyrimidine metabolism (p-value = 3.66E-4, and p-value = 0.00994,

respectively); metabolites within this cluster tended to exhibit increased detection, and, further,

demonstrated dose-dependence with the increasing concentration of supplemented signaling

molecule-mimic, dibutyryl-cAMP. MSEA of the second parental cluster indicated possible

associations with phenylalanine/tyrosine metabolism, glycerolipid metabolism and glycolysis. The

majority of analytes co-localized within this cluster exhibited relatively low to no change in

detection for the lowest-administered dibutyryl-cAMP concentration, while demonstrating

increased-detection relative to control values across the other two experimental conditions. In

consideration of the third parental cluster, MSEA indicated associations with protein biosynthesis,

the urea cycle, ammonia recycling, and glutamate metabolism. This parental cluster was the

largest of the four and displayed minimal change. Because the fourth parental cluster only

contained three analytes (i.e., uridine, D-gluconate, sucrose), MSEA was not performed.

However, patterns across the experimental conditions were still present, and this pattern could

be described as a “normal”-shaped distribution of relative detection across conditions, determined

by the increased detection of the central experimental condition (1.5 mM dibutyryl-cAMP) and

21 Pereyra, et al. examined the influence upon post-germination morphogenesis and cAMP-PKA signaling in Mucor rouxii using extracellular, synthetic induction with various cAMP-analog molecules (Microbiology 2000). N6-benzoyl-cAMP and N6-monobutyryl-cAMP were the primary analogs employed; dibutyryl-cAMP was purportedly used as a control, having been examined at varying concentrations in the same fungus and fungal life-state in a prior publication (Exp Myc 1992), alongside two common hydrolytic byproducts (i.e., O2’-mono-butyryl-cAMP and butyrate), all used at a single, fixed concentration.[257], [258]

35

adjacent, bilateral decreased detection for the other two conditions. The supernatant differential

pH, relative secreted protein, and microscopic imaging acquired for these samples also suggested

dose-dependent effects across all observation-types for this regime of extracellular perturbation

(supplemental figures, B2.7c, B2.7d, and B2.8).

Adjustments of Starting pH.

Environmental proton concentration (pH) is a fundamental abiotic factor that drives, is exploited

and subverted by organisms in their survival, adaptation, and acclimation, as well as their

maintenance of physiological homeostasis [124][125]. The requirement of proton gradients and

individual protons is universally conserved throughout all forms of life and for a large variety of

utilities. The proton’s instrumental roles throughout forms of life is conferred primarily by its

inherent simplicity, presence throughout both inorganic and organic chemical reactions, and its

versatility as a source for ionic potential, chemical and kinetic energy, in addition to its capacity

for mediation of cell-signaling [126], [127]. Chemical gradients, in particular, created by active

transport of protons across a cellular membrane are often regarded as the fundamental tenet of

homeostasis, and, thus, their maintenance frequently presents as the key feature distinguishing

living from dead organic material [128]. Some organisms, like fungi, demonstrate particularly-

blurred lines delineating their physiological homeostasis and strategies of resource acquisition,

which results in emergent, exquisitely-orchestrated relationships and inextricable linkage of these

two facets defining an organism’s life cycle. Specifically, fungi are dependent upon chemical

gradients for facilitated diffusion of nutrients, as well as the activities mediated by various,

specialized transporter proteins [106],[129]. As a result, fungi often demonstrate a sigmoidal

growth curve with a biomass accumulation rate exhibiting a binomial shape over time, given that

limiting nutrients are not unlimited [109]. This relationship in filamentous fungi even governs the

rate at which individual cells reach the “death phase” of growth — this fatal predisposition and

subsequent induction spreads radially from the mycelial center outward, the greatest incidence of

death phase induction being most-central and the least tailing the metabolically active edge of the

hyphal collective [106]. Moreover, an emergent character of this physiological fluidity of fungi with

their surroundings is that secreted products and chemical characteristics of substrata often also

mimic or reflect the trends and points of flection exhibited by these growth curves. Commonly

affected chemical traits of the substrata include pH, a decreased measure of which is often

associated with biomass accumulation and secondary metabolite production in fungi; this trend

applies, both, in the general sense, across fungi, and specifically, as this dynamic has also been

demonstrated by those within the species complex discussed in this work, O. unilateralis s.l. [130].

36

To examine the impact of starting pH upon media alteration in O. kimflemingae, end-point

exometabolomic assays were performed comparing the resulting profiles for three distinct starting

pH values (i.e., pH = 4.2, 5.0, and 6.6, +/- 0.02) to that of an inoculated control (average pH ~5.8

+/- 0.05). As before, a heatmap was generated for visualization of the exometabolomic data,

which allowed for clustering of analytes detected across experimental conditions prior to pathway

enrichment analysis of respective parental clusters and subsets (Figure 2.10). Again, similar to

the other experimental series within this subsection, analytes were grouped into four parental

clusters for trend and metabolite set enrichment analysis. These parental clusters can be

described by their general trends across analytes [peak areas log2-transformed relative to control

values] and in how they compare between experimental conditions. The first of four parental

clusters remained largely unchanged within the pH 4.2 and pH 5.0 experimental conditions, while

demonstrating universal increase across analytes of the third condition, pH 6.6. Additionally, this

parental cluster only contained three analytes (i.e., L−argininosuccinate,

S−adenosyl−L−homocysteine, and GMP), which rendered any attempt at MSEA impractical or

irresponsible. However, it should be noted that the relative-increased detection in pH 6.6

experimental condition of these three metabolites is indicated as nearing 6 log2 fold-change

increase relative to control values, which stands as a distinguishing trend compared to all other

shown analytes and experimental conditions.

The second of the four parental clusters demonstrated very little to no changes between

the three experimental conditions. This cluster contained a total of 37 distinct metabolites, and

MSEA of which indicated enrichment of pathways associated with protein biosynthesis and the

urea cycle (p-value = 6.48E-10, and p-value = 0.00996, respectively). The third of four parental

clusters exhibited analyte behavior demonstrated by pH 4.2 and pH 5.0 experimental conditions

that appeared largely similar in their overall-increased relative detection, while all analytes of the

pH 6.6 condition exhibited relative-decreased detection. MSEA of this parental cluster, which

contained 19 analytes, indicated associations with beta-alanine metabolism, pyrimidine

metabolism, and protein biosynthesis (p-value = 1.27E-4, p-value = 0.00745, and p-value =

0.00799, respectively). Experimental condition two (i.e., pH 5.0) was the only one to exhibit any

exceptions to the parental cluster’s largely positive trend. These two analytes demonstrating high-

contrast as a result of relative-decreased detection, aconitate and uracil, were noted, as they are

known to contribute to the citric acid cycle and pyrimidine metabolism, respectively.

The fourth of four parental clusters possessed analytes across all experimental conditions

exhibited a trend reflecting relative-decreased detection. This parental cluster contained 10

37

analytes, total, and MSEA of this cluster indicated associations primarily with

phenylalanine/tyrosine metabolism; however, connections to aspartate and glycerolipid

metabolism were also indicated. Analytes within this parental cluster showed a relative-decreased

detection across all experimental conditions and metabolites. The only experimental condition

exhibiting trend-exceptions was the second (i.e., pH 5.0), of which dihydroorotate was

demonstrated as having a relative-increased detection, a disruption to the overall negative trend

of the parental cluster. Differential pH and secreted protein were also measured at end-point for

this set of extracellular perturbations (supplemental figure, B2.7e; supplemental figure, B2.7f).

Differential pH and secreted protein demonstrated only minor changes, however, morphological

comparisons revealed a possible influence of starting pH upon this aspect of development

(supplemental figure, B2.8).

Minimal Media Development and Trial – Determining Nutritional Requisites for

Parasite Growth. Minimal medias are essential tools in the laboratory study of any microbial organism, most notably

to mitigate or potentially eliminate isolate contamination, a frequent issue experienced in the lab

(unpublished; respective facility-associated discussion and housekeeping notes)22. For this

reason, a major objective of this work—in addition to better understanding this EPF in the context

of its ecology and basic physiology—was the development of an improved, selective minimal

media. As a result of preceding analyses, auxotrophisms and prototrophisms were therein-

implicated, and, decidedly, were re-examined for their validity through the formulation of selective,

minimal medias.

To develop a minimal formula, data collected as a result of the preceding studies of O.

kimflemingae were used to determine essential nutrients that were selectively removed from the

respective assays’ rich medium (i.e., Grace’s Insect Medium). Results already evaluated in this

work highlighted a simple minority of analytes as having been removed from the medium. These

select few formed the baseline composition23, subsequently-discussed by way of macronutrient

category, and were limited to the following list of components at minimum: asparagine,

methionine, sucrose, and thiamine (Table 2.1). Other key components used in the baseline

formulation were inorganic ions, the concentrations and identities of which mirrored those featured

22 It is important to note that contamination of isolates has been demonstrated to be a problem primarily for the North American isolates, whereas those isolated from South America and/or Southeast Asia do not seem to exhibit this problematic predisposition. 23 “baseline composition” here and throughout refers to the common core of ingredients (with the exception of thiamine, which is doubled in the context of one of the formulations) used in the formulation of various minimal medias tested for viability.

38

in the original Grace’s Insect Medium formulation. Additionally, it should be noted that α-

ketoglutarate was also included in these formulations, despite suggestions of only minor

importance in its observed removal. Several organic acids were included within the rich media

formulation and α-ketoglutarate was known to be located upstream metabolically of these other

organic acids, and, as a result, was determined representative of this constituent category, thusly

validating its inclusion (Figure 2.2b). To examine the viability of the minimal formulations derived

from the results of the prior analyses, each was formalized, prepared and tested in parallel against

the original rich medium, Grace’s Insect Medium. The design of this experiment was identical to

those used to further examine the substrata dynamics and extracellular perturbation-responses

of O. kimflemingae through implementation of end-point bioassays following an incubation period

of 11 days post-inoculation (11 dpi). In addition to the exometabolomic analysis of resultant

samples, the pH and secreted protein of each supernatant were determined.

Visualization of the resulting targeted exometabolomic analyses demonstrated blatant

differential clustering of the formulations used, arranging the four minimal media formulations

amongst themselves, separate from that of the original rich media (Figure 2.11; Table 2.1).

Because each formulation was represented relative to its respective control, the data also

illustrate within-group changes which emphasize the differential traits between the experimental

formulations. Just as before, across all media formulations, a select few components were

indicated as being removed almost entirely from the media: asparagine, thiamine, and sucrose.

Methionine was also nearly depleted across all formulations with the exception of the original rich

media. These four analytes neatly composed the fourth parental cluster of four and MSEA of

which indicated associations with protein biosynthesis (p-value = 0.00294). The third parental

cluster contained a total of 12 analytes, and MSEA of this parental cluster indicated associations

with the citric acid cycle, biotin metabolism and intracellular signaling (i.e., histamine). The second

of four parental clusters, containing a total of 20 analytes, was indicated as being primarily

associated with protein biosynthesis, the urea cycle, arginine/proline metabolism,

phenylalanine/tyrosine metabolism, and ammonia recycling with MSEA (p-value = 4.1E-13, p-

value = 9.56E-4, p-value = 0.00268, p-value = 0.003, and p-value = 0.602, respectively). The first

and the largest of the four parental clusters contained a total of 31 analytes. MSEA of the entire

parental cluster indicated associations with nucleotide sugar metabolism and beta-alanine

metabolism (p-value = 0.00258, and p-value = 0.00801, respectively).

Overall, it was clear by this visualization of the data that indications symptomatic of DNA

replication and general growth are still present across all media formulations. Despite the baseline

39

composition of the minimal medias having included only five macronutrients, the production of

essential vitamins and amino acids did not appear at all inhibited. Further, although pyridoxine

was withheld from inclusion in the baseline composition for the minimal media formulations (i.e.,

those that did not include RPMI vitamin mix), the organism was clearly not deterred from

producing metabolites known to require pyridoxine for their production, either as an organic

cofactor or as a precursor. The exometabolomic signatures of the various minimal formulations

suggest that the few macronutrients included were those which were most influential in their

limitation of this parasite’s growth and development in vitro. Additionally, a macronutrient included

in the minimal medias that had been suspected of lower importance, α-ketoglutarate, was

confirmed as lacking necessity, as its detection across minimal medias was not indicated to have

changed relative to each formulation’s control values. In the case of the rich media, a small

collection of additional analytes was demonstrated to have relative-decreased detection, which

included alanine, pyroglutamic acid and isoleucine; because these were also produced in the

minimal medias which did not include these analytes by formulation, they or the presence of other

nutrients were suggested to be, either, putatively inhibitory of the pathways leading to their

production, or merely a result of ion suppression (analytical influence). Furthermore, this

suggested that their presence could have possibly stifled other routes of metabolism affiliated with

the roughly 22 primary and/or secondary metabolites exhibiting increased-detection in the minimal

medias but that were not detected in such fashions in the rich media.

The pH for each respective formulation was measured and adjusted to a pH of 5.78 +/-

0.02 prior to inoculation and start of incubation period. After the completion of the incubation

period, each supernatant was measured again for pH, as well as secreted protein (supplementary

figures, B2.7g and B2.7h). For this series, each differential pH was normalized by the differential

pH determined for the original rich media formulation (∆pHO). The minimal formulation which

exhibited the largest negative differential pH possessed a collective of inorganic ions incorporated

from a recipe originally intended for Isaria spp. Blastospore Production (IBP) with the exception

of cobalt, and is so-titled [32]. This recipe was unique amongst formulations and distinct from the

traditional salts featured in Grace’s Insect Medium in its replacement of MgCl2, KCl, NaHCO3 and

NaHPO4 with FeSO4, MnSO4 and ZnSO4 (Table 2.1). Following “Minimal + IBP salts”, which

demonstrated the largest magnitude decreased pH relative to control, the measures of “Minimal”,

“Minimal + RPMI + B1”, and, finally, “Minimal + RPMI”, ranked from greatest to least, respectively.

Contrastingly, the secreted protein did not mirror these changes, showing that “Minimal + RPMI”

demonstrated the least difference from the control. Protein secretion diminished from “Minimal”

40

to “Minimal + IBP salts”, and, finally, with “Minimal + RPMI + B1” exhibiting the least amount of

secreted protein relative to control.

2.5 Discussion.

In this study, a targeted exometabolomic strategy within a comprehensive ecophysiological

framework allowed for characterization of a complex fungal parasite using only its respective

growth media. These methods enabled the determination of key metabolites associated with

growth and underlying metabolic pathways of the parasite. Furthermore, they also facilitated

advanced comparisons of two, distinct species, highlighting core physiological differences

between them in the process.

As a result of this study, it was shown that O. kimflemingae and a closely-related species

of the same species complex, O. camponoti-floridani, possess different metabolic capacities,

particularly as it relates to carbon acquisition. Differential preferences for key carbon-sources

were largely implicated by the observable contrast in sugar-utilization and the characteristics of

the remainder carbohydrates. O. kimflemingae appeared able to hydrolyze the nonreducing

disaccharide, sucrose, that was present in the media, as well as absorb the phosphorylated form

of one of its resulting monomers (Figure 2.7). Exoenzymatic phosphorylases are common in fungi,

and, when acting upon sucrose, are known to result in D-fructose and α-1-D-glucose-phosphate

[131], [132]. Excreted invertases (or specific enzyme for hydrolysis of sucrose) have also been

observed in other, closely-related Hypocrealean fungi [133]. It is the continued activity involving

the phosphorylated hexose sugar in the presence of the plateaued-abundance of a simple hexose

that suggests differential treatment of the two monomers by O. kimflemingae. In light of previous

studies having shown the same preference in a related species, the phosphorylated monomer

used in this case was likely glucose-phosphate [134]. If this holds true, the remaining plateaued

hexose is likely fructose, left unutilized by O. kimflemingae. Further proffering this argument

against fructose as preferred carbon-source for this species, another simple sugar present,

ribose, was used instead, despite the former’s abundance; however, it should be noted that the

original source of ribose within the media is unclear. O. camponoti-floridani was suggested, either,

to lack this exoenzymatic sucrose phosphorylase (or invertase) or lack a specialized transporter

facilitating direct uptake, for the abundance of sucrose present within the media went undisturbed

across all observations (Figure 2.7). Similarly to what was observed in O. kimflemingae, the free

hexoses and ribose were used gradually over time, suggesting transporters compatible with these

simple sugars are expressed by O. camponoti-floridani. Interestingly, extracellular trehalose-6-

phosphate (T6P)—labeled as such in the provided figures, although it could be any nonreducing,

41

C-12, homodimeric, phosphorylated sugar—was observed to increase in the presence of O.

camponoti-floridani. Many organisms, including fungi, use trehalose to avoid freezing and

desiccation [135]. Trehalose has also been found in fungi under normal conditions and many fungi

commonly express T6P phosphorylase and T6P synthase; however, T6P has only been

demonstrated to accumulate intracellularly, not extracellularly, when the organism is undergoing

stress [136]. This has also been demonstrated to be a key facet of fungal gluconeogenic- and

glycolytic-regulation [137], [138]. Trehalose homeostasis has also been associated with virulence

and stress tolerance in, both, human and plant pathogens (e.g., Cryptococcus gattii and

Magnaporthe oryzae, respectively) [139], [140]. Because of this molecule’s intracellular functional

importance, the extracellular detection of this compound is most likely the result of cellular

leakage. If this is the case, its measurement within the media could function as a proximal metric

for stress and cellular antagonism in vitro. Alternatively, if it was intentionally secreted, the

compound could serve as a signaling molecule of sorts for, either, intercellular regulation of

colonial growth or for communication with host tissues. Other interesting sources of carbon

backbones within the media, namely, TCA cycle intermediates, were used distinctly between the

two species, with the exception of α-ketoglutarate. These interspecific distinctions in the use of

TCA cycle intermediates may implicate differences in canonical TCA metabolism, such as the

utilization of a glyoxylate shunt. To illuminate the full-extent of their biological functions and value,

further characterization should be considered for these select carbon skeletons.

O. camponoti-floridani appeared to produce a greater diversity of central carbon

intermediates and consumed a greater number of amino acids than that demonstrated by O.

kimflemingae, highlighting metabolic preferences between species. This preference is not

surprising considering and could even be the direct result of the low abundance of free-hexoses

available for uptake, assuming the species possesses a deficit of a proper sucrose phosphorylase

similar to what was purportedly exhibited by O. kimflemingae. Putatively-secreted analytes

demarcate precursing- and terminating-points of the shikimate pathway24, the glyoxylate cycle25

and those feeding into/out of gluconeogenesis (e.g., phosphoenolpyruvate), suggesting that O.

camponoti-floridani is likely distributing gluconeogenic products between multiple endpoints.

24 The shikimate pathway is used by many microorganisms to synthesize cyclic amino acids from phosphoenolpyruvate and/or other products of glucose oxidation. 25 The glyoxylate cycle is a pathway common to microorganisms and plants that functions as a shortcut or shunt within the TCA cycle, of which allows for the conservation and production of carbohydrates via omission of the traditional cycle’s lost CO2.

42

Despite these differences, it appears that both of these fungal species have the capability to use

a large array of carbon sources, although, one more-so than the other.

Amino acid metabolism was differential between the two species; however, overlap

existed for a select few, suggesting that these specific amino acids are essential for both O.

camponoti-floridani and O. kimflemingae. The similarities in asparagine utilization implicate a

preferential reliance upon versatile glucogenic amino acids, while methionine consumption

suggests a priority for dual-purpose resources, as this latter compound can act as an organic

source of both nitrogen and sulfur [113]. Interestingly, organic sulfur and nitrogen assimilation has

been associated with pathogenic life history traits in other fungal species [141]. Additionally, both

asparagine and methionine have been linked to stress tolerance in pathogenic microbes,

including fungi [142], [143]. Asparagine possesses the highest ratio of nitrogen to carbon of any

uncharged polar amino acid, a combination of traits which better enables facilitated and passive

diffusion of amino acids into the protoplasm and has also led to asparagine’s mention as a

“perfect” nitrogen source for certain fungal pathogens [144]. Further, asparagine is highly-

centralized in microbial metabolisms, and is the only identified auxoheterotrophic amino acid of

the human fungal pathogen, Paracoccioides spp. [145]. Asparagine is also known for its role in

cross-linkages of fungal glycans, microbial products well-known as crucial governors of microbe-

microbe and microbe-host associations [146]. Methionine, on the other hand, plays an active role

at the crossroads of several key metabolic pathways [147], and is a known transcriptional

regulator in fungi [148]. Methionine is the primary precursor of S-adenosylmethionine (SAM), a

molecule that functions as a major preceding compound for microbial secondary metabolites, and

acts as the dominant methyl-group donor for epigenetic modulation and post-translational

modifications [149]. In many fungal pathogens, methionine is crucial for mediating host-infiltration,

colonization and subversion of host immune responses [150]; it is also known to confer stress

tolerance and virulence by enabling the production of stress-ameliorating compounds (e.g.,

glycerol, glutathione), fungal virulence factors and secreted toxins, respectively [143], [151].

Together, auxotrophy for these two amino acids may suggest strict genetic regulation that may

also be enabling of metabolic remodeling, as well as the production of important proteins and

secondary metabolites associated with virulence and pathogenesis [152].

The distinctions between O. kimflemingae and O. camponoti-floridani regarding putatively-

secreted amino acid metabolites and secondary metabolites implicated differences in probable

host-niche, as well as respective ecologies and pathogenic traits. O. camponoti-floridani

exercised additional uptake of several other amino acids that spanned nearly all amino acid

43

categories. Although fungi are known to uptake both inorganic and organic sources of nitrogen, a

preference for organic nitrogen sources has been frequently associated with pathogenic fungal

species [153], [154]; further, increased access to organic nitrogen sources over inorganic ones

has been shown to increase rates of proliferation and biomass accumulation for those species

[155]. In addition to more robust indications of glycogen-catabolism than those observed in O.

kimflemingae—another physiological symptom documented to have importance in fungal

pathogenesis [153]—this expanded range of organic nitrogen sources could indicate a greater

predisposition for virulence O. camponoti-floridani compared to its relative tested in-parallel.

Similarly, the highly-selective nature of amino acid utilization observed in O. kimflemingae could

support the notion that it is more specially-adapted to its host species. If we hold true, both, the

ecological assumption that symbiotic relationships trend toward the mutualistic [156] and that the

virulence of O. camponoti-floridani is in fact comparably more impressive, then it can be

hypothesized that this subtropical species possibly adopted its host-niche much more recently in

evolutionary history than O. kimflemingae. In the case of the latter species, a regulatory bottleneck

is likely being conferred through the selective uptake of asparagine over that of other

metabolically-versatile amino acids, like aspartate, glutamate or glutamine—all for which O.

camponoti-floridani demonstrated auxotrophisms [157]—and, as a result, may make the

parasite’s metabolic activity and presence within its host more salient.

Certain vitamins (organic cofactors) were found to be important for the growth of both

species, O. kimflemingae and O. camponoti-floridani. Each was highly auxotrophic in regard to

thiamine (Figure 2.7c), a cofactor vital to central carbon metabolism and the prototrophism for

which is relatively common in bacteria and some fungi [158], although, auxotrophism for this

vitamin appears common across Ascomycetes [129]. Thiamine has been found to be essential

for virulence in fungal pathogens [159], [160], and has become a popular focus for development

of antifungals [161], [162]. Additionally, both species were shown to be prototrophic for nicotinate

and pantothenate (Figure 2.7c). Pantothenate can be synthesized de novo by Saccharomyces

cerevisiae from polyamine degradation-dependent β-alanine production [163], and is the pathway

of choice for fungal biosynthesis of CoA and acetyl-CoA, which are the primary metabolites used

in the production of non-ribosomal peptides and polyketides [164]. Interestingly, NADPH, a

bioactive product of nicotinate metabolism, is also required for the biosynthesis of these important

secondary metabolites. Secondary metabolites dependent upon these pathways include fatty

alkyl pantothenate analogues (pantothenamides), which are known for their ability to interfere with

fatty acid biosynthesis in bacteria, a mode of action known to confer antibiotic-like qualities [165].

The biosynthesis of pantothenate in both species appears at-odds with the differential

44

auxotrophisms exhibited for its potential precursors, alanine and β-alanine (Figure 2.7d). O.

camponoti-floridani is the only of the two which appears to be directly capitalizing upon the known

pathway of fungal β-alanine biosynthesis for the production of pantothenate. In contrast, the

exometabolomic behavior of O. kimflemingae suggests that it is, either using alanine to

biosynthesize aspartate, which is then being fed into the traditional route of pantothenate

biosynthesis, or alanine is not being used for this purpose and pantothenate’s precursor, β-

alanine, is being produced through polyamine degradation (i.e., spermine and then 3-

aminopropanal degradation). Alternatively, use of valine for the production of the pantothenate

precursor, pantoate, has also been confirmed as a contributor to this biosynthetic product in fungi

[166], [167]. Due to the importance of auxotrophisms in determining host-parasite relationships,

it would be immensely helpful if these pathways for both species were further-elucidated. Both

species seemed to be somewhat facultatively-auxotrophic for pyridoxine, although, differing in the

exact manner of which (Figure 2.7c). Methyltransferases (e.g., SAM) and aminotransferases—

which enable the interconversion of various amino acids and contributors to central carbon

metabolism, as well as the production of secondary metabolites—require pyridoxine as a cofactor

for normal enzymatic function [168]. O. kimflemingae was shown experimentally (i.e., testing of

the formulated minimal medias) to not necessarily require pyridoxine for normal blastoconidial

proliferation; however, this vitamin and its requisite should be further evaluated to realize its true

necessity in the case of O. camponoti-floridani.

As suggested by various contributions to Hypocrealean literature, several basic inorganic

ions are absolutely required for growth of varieties of Ophiocordyceps [35]; for O. kimflemingae,

this hypothesis was recapitulated in the results of the minimal media trials, in addition to several

experiments which simulated the removal/deficit of select inorganic ions and matched with

complementary rescue experiments (Figure 2.3a). Interestingly, macrominerals showed very little

sway in physiological impact in the incidences of both extracellular and cell-permeable chelators,

while trace metal ions (e.g., copper or zinc) were highlighted as inorganic ions of physiological

significance in other identical regimens. Results of the minimal media trials further corroborated

the importance of trace metal ions over that of macrominerals in liquid culture, as the formulation

titled “Grace’s Minimal Media with IBP Salts”—which contained a broader spectrum of trace metal

ions but lacked all vitamins with the exception of thiamine—demonstrated an exometabolomic

signature nearly identical to the minimal media (traditional inorganic ion

composition/concentrations and all of the vitamins included in the original rich media formulation).

A large number of the trials resulted in the apparent death or inhibited growth of the fungus,

suggesting inhibition or disruption of essential nutrient acquisition or underlying metabolic

45

pathways instrumental to survival. In response to this, an additional pair of figures was generated

to demonstrate the metabolic footprints that are implicated to represent “living/proliferating” and

“no growth” as they relate to these trials (Figure 2.11). Moreover, it is recommended that these

trends be further examined through similarly-assayed experiments specially designed to test the

impact of drop-out media variants, namely, those of the improved minimal media formulation

herein derived.

46

2.6 Featured Tables.

Table 2.0 Grace’s Insect Medium – Official Formulation.

47

Table 2.1 Minimal Media Formulations.

48

2.7 Featured Figures.

Figure 2.0a Photographic Diagram of the Life Cycle of O. unilateralis s.l.

49

Figure 2.0b Schematic of in vitro Growth Assays.

50

Figure 2.0a-b Introductory Model of Study System and Experimental Design. (a) Photographic Diagram of Life Cycle of O. unilateralis s.l. This figure depicts the general life cycle of

this species complex of fungi in a photographic diagram. The life cycle traditionally begins with infection of

the host, leading to climax of infection (~1-2 weeks post-exposure). The host is then compelled to affix itself

within the canopy of its indigenous, forest habitat, and dies within a few hours of this process. The fungus

then is able to become necrotrophic with this host-termination, eventually producing a teleomorphic

structure, complete with ascus at the point of maturity. This structure then produces infectious spores that

are dispersed to infect new hosts. (b) Schematic of in vitro Growth Assays. Two primary methods were

used in the implementation of metabolic footprinting of parasites in vitro: 1) longitudinal and 2) end-point.

Longitudinal assays were performed with one biological replicate of each species (see methods for explicit

details). End-point exometabolomic assays were performed in a similar technical and analytical fashion,

but were only performed once at the termination point of an incubation period (11 dpi, for all series).

51

Figure 2.1. Longitudinal Exometabolomic Data for O. kimflemingae, in vitro.

52

Figure 2.1. Longitudinal Exometabolomic Data for O. kimflemingae, in vitro. A tissue plug of O. kimflemingae was inoculated into Grace’s Insect Medium and extracellular metabolites

were extracted in technical triplicate every 24 hours over the course of 21 days (in addition to day 0).

Targeted metabolite peak areas, reflective of relative abundance, were log2-transformed relative to an

average non-inoculated control, values which were averaged over the course of the experiment; this non-

inoculated control was mock-cultivated in-parallel to the inoculated flask under identical conditions.

Metabolomic data is displayed as a heatmap using Euclidean distance and Ward clustering methods.

Contrastingly to what was performed with the analytes (y-axis), samples (x-axis) were left unclustered in

order to show the changes over the course of the experiment, chronologically. The horizontal axis displays

the detected metabolites and the vertical axis displays the time in days. Analytes highlighted in red are

known components of the used growth medium, a standard Grace’s Insect Medium formulation (Table 2.0).

Asterisk indicates a special case of analyte which is not officially noted within the formulation, but is

understood as a common byproduct of the analytical method used, here; namely, when samples contain

glutamine and/or glutamate, the respective analyte’s precursors. All samples were collected in technical

triplicate for a single biological sample (n = 1). All analytes shown passed relative standard deviation

filtering (<25%) across all samples.

53

Figure 2.2a Primary Carbon Sources.

54

Figure 2.2b TCA Cycle and its Contributors.

55

Figure 2.2c Focal Amino Acids.

56

Figure 2.2d Urea Cycle and Pyrimidine Synthesis.

57

Figure 2.2e Vitamins – Pantothenate and Precursors.

58

Figure 2.2f Vitamins – Nicotinate Metabolism Requires Pyridoxine.

59

Figure 2.2a-f Exometabolomic Data in Physiological Context. Metabolite data from the O. kimflemingae longitudinal study were re-visualized based on their respective

trends and potential biological interactions. Accordingly, boxplots for each of the analytes of interest were

placed into a schematic representation of their putative physiological contexts. For each envisaged boxplot,

the y-axis denotes log2-fold change relative to control values, while the x-axis indicates time in days post-

inoculation (dpi). (a) Depiction of primary carbon-sources and their select relevant catabolites. (b) Figure

illustrating the TCA cycle and facets of primary carbon metabolism. (c) Amino acid metabolism as it relates

to sulfur regulation. (d) Illustration of the urea cycle and precursors of pyrimidine biosynthesis. (e)

Pantothenate biosynthesis. (f) Precursing amino acid and cofactor required for nicotinate biosynthesis. All

boxplots were generated with the MetaboAnalyst.ca web portal (Statistical Analysis, core option) using the

data from Figure 2.1.

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Figure 2.3a End-point Exometabolomic Metaprints for Supplementation and

Chelation of Trace Ions.

61

Figure 2.3b Heatmap of Metacluster Bases Underlying Suprahexagonal Metaprints.

62

Figure 2.3c Metacluster Bases, Node Assignments and Pathway Enrichment Index.

63

Figure 2.3 Suprahexagonal Mapping of End-point Trace Ion Assays. (a) End-point Exometabolomic Metaprints for Supplementation and Chelation of Trace Ions. Series

of end-point exometabolomic studies were performed upon the substrata of O. kimflemingae.

Suprahexagonal mapping using an unsupervised learning algorithm (i.e., self-organizing maps) was used

to visualize the resultant exometabolomic data and compare metabolic footprints of the various

experimental conditions (supraHex, R-package; Bioconductor). Analytes to which this technique was

employed were filtered prior to mapping (relative standard deviation, < 25%). All data was log2-transformed

relative to an average [non-inoculated] control per series, respectively. Each experimental condition

possessed a biological replicate of one (n = 1) and all extractions at end-point (11 dpi) were performed in

technical triplicate. Three types of general conditions were tested: 1) trace ion supplementation; 2) trace

ion chelation using TPEN or EDTA; and 3) simultaneous rescue of growth phenotype through equimolar

administration of individual trace ions and a partner chelating agent. Supplementation conditions are listed

within the inlaid table (i.e., “Supplement Concentrations”), and denote series number within parentheses.

Series 5 chelator titrations are also listed in an inlaid table featuring the respective agents’ concentrations

(i.e., “Titrations”). All experimental conditions resulting in apparent “death” or lack of growth at end-point

are indicated by light gray labels. (b) Heatmap of Metacluster Bases Underlying Suprahexagonal

Metaprints. Using the same package and within the same workspace, the data was then used to generate

a heatmap based upon the corresponding analytes and their respective metaclusters across experimental

conditions. The x-axis shows the experimental conditions (series number in parentheses), and the y-axis

denotes detected analytes (right) and their respective metacluster base (left). The base colors are featured

in an additional key with their assigned base number. (c) Metacluster Bases, Metabolite Node

Assignment and Pathway Enrichment Index for Metaprints. This figure was generated as a numerical

metabolite-node index (note cell numbers). Each metacluster base is denoted by the colors of the index’s

suprahexagonal cells, each of which contains a set of analytes assigned per metabolite-node. The colors

of the bases and their respective designated number are conserved from the preceding figure. For each

metacluster base, all analytes contained across all base-respective cells were used to perform enrichment

analysis. The resulting pathway associations of these analyses are described within this index, adjacent to

each metacluster base. All enrichment was performed using the MSEA tool provided through the

MetaboAnalyst.ca web portal.

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Figure 2.4 Comparative Metaprints for Two Hypocrealean Species.

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Figure 2.4 Comparative Metaprints for Two Hypocrealean Species. The upper portion of this figure displays a pair of metaprints attributable to the final experimental day (21

dpi) for the metabolic footprints of two infraspecific fungi. O. kimflemingae is denoted by “OKf” and O.

camponoti-floridani is shown as “OCf”. These maps are used for all subsequent feature exploration and

analyses performed for this infraspecific comparison. The complete collection of incremented

suprahexagonal maps per each species’ longitudinal exometabolomic data, from which these (above) maps

were sampled, were generated using the supraHex package in R (Bioconductor). The lower portion of this

figure exhibits the aforementioned maps’ index key, metacluster bases (each indicated by color and

adjacent, colored numbers), and the respective bases’ pathway-enrichment associations. All analytes

contained within each metacluster base were used to determine respective metacluster pathway

enrichment. These analyses were performed with the use of the MSEA feature, available through the

MetaboAnalyst.ca web portal.

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Figure 2.5 Longitudinal Exometabolomic Data for O. camponoti-floridani, in vitro.

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Figure 2.5 Longitudinal Exometabolomic Data for O. camponoti-floridani, in vitro. This heatmap was generated using longitudinal exometabolomic data of O. camponoti-floridani. For 21

days post-inoculation (samples include day 0), media was sampled and methanol-extracted in triplicate for

LC-MS analysis (biological replicate, n=1). Metabolites are denoted along the y-axis (right, vertical), while

the experimental days of observation are represented along the x-axis (bottom, horizontal). Dendrogram

clustering was performed with the use of Euclidean distance and Ward linkage methods. All peak area data

were normalized using the average baseline value per analyte; subsequently, these data were filtered by

their respective relative standard deviation (RSD < 25%). Data shown are these values, log2-transformed

relative to the average control values. Heatmap was generated using the advanced heatmap function

provided by the supraHex package in R (Bioconductor).

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Figure 2.6 Metatracks ‒ Tracing Metabolic Footprints through Time.

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Figure 2.6 Metatracks ‒ Tracing Metabolic Footprints through Time. O. kimflemingae (OKf) and O. camponoti-floridani (OCf) were sequentially mapped onto a suprahexagonal

plane for all increments of their respective experimental periods. Both sets contained 287 analytes

regardless of absence or presence. Further, both data sets were RSD-filtered and log2-transformed relative

to control values prior to incorporating the complete list of analytes into each and the subsequent zero-

imputation for any absences. The longitudinal exometabolomic data for OKf was utilized as the training

data for the employed algorithm (self-organizing maps, SOM). The longitudinal exometabolomic data for

the second species, OCf, was overlain onto the same, hyper-dimensional, topologically-fixed plane to

generate its respective metatracks. Suprahexagonal mapping was performed using the supraHex package

in R (Bioconductor).

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Figure 2.7a Infraspecific Dual-plot – Comparative Carbon-source Utilization.

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Figure 2.7b Infraspecific Dual-plot – Differential Nitrogen-source Utilization.

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Figure 2.7c Infraspecific Dual-plot – Differential Vitamin Utilization.

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Figure 2.7d Infraspecific Dual-plot – Differential Use of Precursors.

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Figure 2.7 Infraspecific Dual-plots ‒ Dissection of Infraspecific Metaprints. A single metaprint taken from the final experimental day (21 dpi) for each species, OKf (O. kimflemingae)

and OCf (O. camponoti-floridani), are exhibited and annotated, accordingly. With each of the subsequently

listed figures, a macronutritional category is highlighted and respective analytes reviewed, each shown in

more explicit detail in an accompanying plot immediately adjacent to the pair of metaprints. Metabolites

represented with each plot are indicated by their respective labels. OKf is indicated by a red box and data

plotted in red, while the second species, OCf, is denoted by a dark blue annotation box and identically-

colored plotted data. For plots, time in days post-inoculation (dpi) is featured upon the x-axis, and log2-

transformed [relative to controls] peak area is represented on the y-axis. In one instance, a plot features

non-transformed peak area for the y-axis; in this case, the control data is also plotted: OKf control data is

denoted by orange and OCf control data is shown in light blue. Metaprint cells containing these specific

analytes were demarcated by darkened edges (small, black hexagons) and respectively labeled according

to their relevant, contained metabolite(s). Contents of focus for this figure are labeled, accordingly, with

each emphasized cell. (a) Select analytes detected between species were compared as they relate to

carbon-source utilization (removal from the media) and central carbon metabolism. The metabolites

featured include sucrose, ribose, hexose and hexose-phosphate, the latter three constituting putative

products given hydrolytic processing of the former. (b) Focal nitrogen sources are highlighted and exhibited

individually in plots that feature the respective longitudinal exometabolomic data from each species. These

metabolites of focus include methionine, asparagine, cysteine and its dimeric form, cystine. (c) Vitamins of

interest are highlighted and examined in more detail in adjacent plots, each of which features the

longitudinal exometabolomic data for both regarded species. Metabolites featured in this figure include

thiamine, pyridoxine, pantothenate, and nicotinate. The last listed was a special case in regard to its

transformed data; therefore, for this single case, the data was shown in its non-transformed peak area state.

This prompted inclusion of the respective control data, which, as referenced before, are indicated

accordingly (see additional, inlaid key). (d) Pantothenate and its precursors for its biosynthesis, as indicated

in the literature as it applies to yeast and other microbes. Log2-transformed longitudinal data for these

analytes are displayed in adjacent plots, OKf being depicted in red and OCf being exhibited in blue.

Aspartate, featured in this figure, is shown in the form of peak areas for both species for parallel visualization

with each respective control dataset (similarly to what was done for exhibition of data for nicotinate).

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Figure 2.8 End-point Exometabolomic Signatures of Asparagine- and Methionine-

fortified Substrata.

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Figure 2.8 End-point Exometabolomic Signatures of Asparagine- and Methionine-

fortified Substrata. After a period of cultivation (11 dpi), asparagine- and methionine-fortified medias were examined using

targeted end-point exometabolomic analysis to derive a substratum-utilization phenotype, or metabolic

footprint. Samples were extracted in technical triplicate (biological, n = 1). After blank-subtraction (average

blank values determine from all blank samples per metabolite), analytes were filtered based on their

calculated RSD values (relative standard deviation, < 25%). Peak areas were then log2-transformed relative

to values of a parallel-cultivated inoculated control; these data were used to generate the above heatmap.

The aforementioned inoculated control was simply a flask of standard-formulation Grace’s Insect Medium

that was inoculated in-parallel with the other experimental flasks containing formulation-variants

(asparagine- and methionine-fortified Grace’s Insect Medium; each fortifying nutrient’s concentration was

approximately double what it was in the medium’s original formula). Analytes are clustered by row across

experimental conditions by way of Euclidean-Ward distance and clustering methods. A total of four

dendrographic bases were permitted emphasis and respective color-assignment through the selective

enabling of cut-tree graphical features. Experimental conditions were left unclustered to aid in legibility of

the figure. Heatmap was generated using the advanced heatmap function included within the supraHex

package in R (Bioconductor).

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Figure 2.9 End-point Exometabolomic Signatures of Signaling Molecule-mimic.

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Figure 2.9 End-point Exometabolomic Signatures of Signaling Molecule-mimic. Flasks containing standard Grace’s Insect Medium were doped with select administrations of a synthetic

cAMP-mimic, dibutyryl-cAMP (i.e., one flask each: 1.0 mM, 1.5 mM, and 1.7 mM). After 11 dpi, the

respective substrata were extracted in technical triplicate for targeted end-point exometabolomic analysis

(biological, n = 1). The peak areas were blank-subtracted (average blank values determined from all blank

samples per metabolite), and, subsequently, the analytes were filtered through use of calculated relative

standard deviation (RSD, < 25%). The resultant data were log2-transformed relative to inoculated control

values for visualization of the analytes in heatmap form (above). Metabolites are indicated by the y-axis,

while the experimental conditions are represented along the x-axis. Analyte dendrographic generation and

row-reordering were performed using Euclidean-Ward distance and clustering methods. Four

dendrographic features were selected for emphasis in the respective dendrogram. Samples (x-axis) were

left unclustered to aid in legibility and inference. This figure was generated with the use of the advanced

heatmap function available in the supraHex package in R (Bioconductor).

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Figure 2.10 End-point Exometabolomic Signatures of Altered Substrata Starting

pH.

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Figure 2.10 End-point Exometabolomic Signatures of Altered Substrata Starting

pH. Standard Grace’s Insect Medium was adjusted to three distinct starting pHs (i.e., 4.2, 5.0 and 6.6 pH).

These were then inoculated and co-cultivated with additional inoculated and non-inoculated controls (each

unadjusted, common-practice pH of ~5.8). All flasks were cultivated under identical conditions and, after 11

dpi, were extracted in technical triplicate for the performance of targeted end-point exometabolomic analysis

(biological, n = 1). Peak area data was blank-subtracted using the determined average noise levels across

samples (average values determined from all blank samples per metabolite). Analytes were then filtered

according to calculated RSD values (relative standard deviation, < 25%). Subsequently, data were log2-

transformed relative to the values of the inoculated control. These results were then used to generate the

above heatmap. The y-axis of this figure displays the detected metabolites (clustered), while the

experimental conditions are represented upon the x-axis. The dendrogram featured along the y-axis was

produced with the use of Euclidean-Ward distance and clustering methods, the structure of which is paired

with the derived base groups, decidedly demarcated as four parental clusters. Experimental conditions (x-

axis) were left unclustered to aid in legibility of the figure. This heatmap was generated through use of the

advanced heatmap function available in the supraHex package in R (Bioconductor).

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Figure 2.11 End-point Exometabolomics Confirms Viability of Minimal Medias.

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Figure 2.11 End-point Exometabolomics Confirms Viability of Minimal Medias. Four distinct minimal medias were formulated based on the preliminary results returned from the

longitudinal exometabolomic study of O. kimflemingae (Table 2.1). These medias were inoculated and co-

cultivated with their respective non-inoculated controls. An additional pair was also cultivated in-parallel to

allow for comparison of the new formulations to that of the original rich medium (Grace’s Insect Medium);

again, one flask was inoculated and the other, its respective control, was non-inoculated. After 11 dpi of

continuous culture conditions, all medias were extracted in technical triplicate for the exaction of targeted

exometabolomic analysis (biological, n = 1). Peak area data was blank-subtracted (blank average values

determined from all blank samples per metabolite), and, then, relative standard deviations were calculated

to filter analytes by reproducibility (RSD, < 25%). The same processing steps were exacted in the case of

the data determined from the pair of parallel-cultivated standard Grace’s Insect Medium formulations (one

inoculated and one non-inoculated). The data for, each, the inoculated standard rich medium and all other

processed inoculated datasets were subsequently log2-transformed relative to their respective formulations’

non-inoculated control data. The above figure was generated with the resultant data using the advanced

heatmap function available within the supraHex package in R (Bioconductor). Analytes are depicted on the

y-axis, clustered (Euclidean-Ward) and accompanied by a dendrogram reflective of that clustering; while,

distinct media formulations (experimental conditions) are denoted along the x-axis. Metabolites in red are

thusly-shaded to indicate that they are known components of the standard Grace’s Insect Medium

formulation (an asterisk indicates that a metabolite is expected in the presence of glutamate/glutamine upon

LC-MS analysis as an artefact of the method; although, this analyte is shaded in red, this specific

component is at no point added to the medium during preparation or formal manufacture). Experimental

conditions (x-axis) for this figure were also clustered (Euclidean-Ward); however, no columnar tree bases

were assigned or otherwise designated to complement the generated dendrogram.

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Figure 2.12 Perturbations Reveal Exometabolomic Signatures of Life and Death.

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Figure 2.12 Perturbations Reveal Exometabolomic Signatures of Life and Death. With the analyses performed upon the data derived from the trace ion/macromineral supplementation,

chelation and phenotype rescue exometabolomic assays, hexagonal mapping revealed putative hexmap

signatures of life and death under the various circumstances. These two planes were generated using the

averaged data for cultures determined as being either “dead” or “alive” at the end-point of the respective

studies (11 dpi for all conditions tested). Using all metabolites in-common between all data sets, the data

were mapped accordingly, resulting in the two distinct data phenotypes shown here. All data were RSD-

filtered (< 25%) and were log2-transformed relative to the calculated means across all experimental

conditions per analyte (group mean-centered). Hexmaps were generated using the overlay feature within

the supraHex package in R (Bioconductor).

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Chapter 3. Summary and Concluding Statements.

3.1 Summary of Findings and Implications for Pathogenic Fungi.

In this study, a targeted exometabolomics technique was applied in the interest of examining a

complex host-parasite system and allowed for successful fulfillment of my formal thesis statement

(ref. Chapter 1). Through this, I was able to highlight the nutritional requirements of this parasite

and compare these with a second, closely related species, which fostered further biological and

ecological inference. Subsequently, I used targeted exometabolomics in combination with other

methods of bioassay to probe tolerances of the parasite and examine its responses, revealing

novel physiological capacities and features, and, as a result, contributed a unique, in-depth

dataset to the current body of entomopathogenic fungal literature. This collected data has allowed

for the expansion of the initial, proposed ecological model (Figure 1.0), generating a more detailed

picture of these interactions as they were illuminated by these in vitro methods (Figure 3.0; Figure

3.1). With these concepts condensed, it is now possible to discuss work-done in the context of

biological and ecological implications, as well as future work to confirm, refute, or expand these

ideas and fundamental questions.

According to the differences in physiology, as well as implicated host-niche and virulence,

this ecological model is likely overly simplistic for the second species, O. camponoti-floridani. If a

traditional compartmental model is considered (supplemental figure B3.0) and adjusted,

accordingly, to emulate the variable states characteristic to this parasite’s characteristic life cycle,

we might more precisely examine just how these two species may differ between their respective

realized host- and environmental niches. First, it is necessary to examine select facets of their

ecologies in order to highlight differences in life history traits. In other Ophiocordycipitaceae, it

has been observed that overwintering of the infected hosts can become a regular facet of the

host-parasite relationship, depending upon the ecoregion to which the two organisms are

indigenous [169]. These fastidious species demonstrate life history traits reflective of both stress-

tolerant (S-selected) and ruderal (R-selected) survival strategy types, which are characterized by

the modulation of resource-competition through environmental stress and/or disturbance,

respectively26. Most fungi require continuous, high humidity and mild temperatures for vegetative

26 In-complement to the r/K selection theory developed by R.H. MacArthur and E.O. Wilson, C-S-R selection theory (developed by J.P. Grime, 1974, 1977) is an evolutionary theory describing the various adaptive strategies of plants. Grime expanded this theory in-address of vegetation life history strategies and succession; this included a reconciliation of r/K-selection theory with that of C-S-R selection theory, ultimately resulting in a triangle-shaped figure visualizing the combinations of survival strategies reflective of organismal investments resulting from the relative importance of competition, stress and/or disturbance. This heuristic represents functional equilibria split between three corners constituted by C-, S- and R-selected strategies [259], [260].

86

growth, a phase which poses great threat to recently-dispersed ascospores. The subterranean

overwintering of O. sinensis, for example, acts to enhance the relative fitness of its sexual spores

upon their dispersal in the early spring, the only time the fungus produces a fruiting body and

finally breaches the soil surface. The seasonality of this species’ teleomorph depends upon this

regular, high-stress disturbance for the mitigation of competition and enhancement of offspring

survival. It is the stress tolerance to conditions presented by the spring season in an alpine climate

that acts as competitive release for the offspring of this species. Contrastingly, the tropical species

of Ophiocordyceps do not exhibit such an extreme dependence upon the seasons, in some cases,

continuously infecting naïve hosts year-round; however, some species, such as O. camponoti-

rufipedis, have adopted distinct strategies that similarly enhance the survival of their offspring,

namely, via arboreal spore release and optimized host-exposure via targeted dispersal [170]

(Figure 2.0). Even though the forest floor of this tropical climate maintains stable high humidity,

the concentration of water vapor begins to drop exponentially and microclimate instability

becomes standard with exit of the boundary layer27 of the forest floor [171]. Normally, this would

pose a great threat to the survival of common fungi; however, species of Ophiocordyceps seem

to have adapted to these more challenging microclimates for the sake of transmission. Both O.

kimflemingae and O. camponoti-floridani exhibit this arboreal dispersal, but, their respective

ecologies differ in a number of ways. First, O. kimflemingae is indigenous to the temperate

rainforest ecoregions of the Southeastern United States, whereas O. camponoti-floridani, a more

recently discovered variety, is known only to the sub-tropical climate of Central and Southern

Florida. In a manner similar to the tropical species found in Northern Brazil, O. camponoti-floridani

appears to perpetuate its life cycle throughout most of the year (unpublished data). Contrastingly,

infections of O. kimflemingae appear to slow prior to and cease during the winter months. In line

with its purported continuous infectious cycles, the increased breadth of amino acids used by O.

camponoti-floridani suggested increased virulence [152], [172]. Virulence is inextricably tied to

transmission and transmissibility of a pathogen [173]–[175]. With consideration for the contrasting

geographies of O. camponoti-floridani (subtropical climate) with that of O. kimflemingae

(temperate rainforest ecoregion), paired with the implications of its broadened organic nitrogen

utilization, it was further substantiated that seasonality of infection cycles may play an important

role in determination of virulence for this species complex. Although continuous infection cycles

for O. camponoti-floridani have been proffered, thorough studies examining this phenomenon

have yet to be performed. The suggestion that increased virulence is more-positively correlated

27 A boundary layer is a layer of air adjacent to a surface characterized by low-turbulence and, therein, low convection; demonstrates minimal response (e.g., heat loss, etc.) as a result of forced convection/laminar flow.

87

with geographical location than it is with the history of host-association proved somewhat

unexpected, as it is understood that host-parasite relationships trend toward attenuation with

longer histories of association [176], [177]. Ultimately, these histories of association need more

thoroughly confirmed to refute these hypotheses. Alternatively, low relatedness of individual hosts

is commonly associated with higher virulence in the event of horizontal transmission [178], and

has been demonstrated in other eukaryotic pathogens that demonstrate multi-host life history

traits [179]. This trend has also been observed in fungal pathogens and has been further

implicated more recently by the positive correlation of codon optimization with increased host

range [180]. However, it is uncertain as to how this might occur in organisms which exhibit life

history dualities dependent upon host-context, like many species of Hypocrealean fungi, which

serve as mutualist endophytes of plants while also presenting as an entomopathogen with insect-

host introduction. Moreover, due to the taxonomic disparities between them, determining the true

evolutionary influence of intermediate hosts can be challenging and further complicates useful

examination of these interactions. Although, in provision of promise, it continues to be suggested

that plants may possess mutualistic relationships with endophytes in which these symbiotic fungi

actively infect herbivorous insects in its host’s defense [181]–[184]. Thence, in consideration of

the infection’s well-known characteristics, it is tempting to posit that O. unilateralis s.l. and similar

fungal species may actually be demonstrating an extended phenotype, not just of themselves,

but of a yet-discovered mutualistic symbiosis. Ultimately, these many considerations suggest that

further study of this species complex may provide previously untapped insight regarding the

evolution of virulence and virulence maintenance/attenuation via intermediaries in highly-

coevolved host-parasite interactions.

As a result of this study, in addition to the contributions of previously published research,

it is becoming clear that these two species demonstrate physiological signatures that complement

their respective ecologies and relationships with their hosts. In particular, these data were found

to recapitulate many traits of ants and certain ant species that corroborate their respective

candidate statuses for parasitism by this particular pathogen (O. unilateralis s.l.). A general

predisposition for infection by the entomopathogen is possibly conferred through the nutrient

density and high thiamine content found across many ant species [185]. This is likely further

supplemented by the relatively high moisture, energy and crude/available protein content

reportedly found in ants compared to other social insects [186]. In regard to the suggested

importance of trace nutrients, as opposed to macronutrients, ants are known to selectively

sequester trace metal ions in both their soft tissues and throughout portions of the cuticle; for

example, zinc can be found at higher levels within the cuticle of the mandibles, and is thought to

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confer hardness for the mechanical utility of the tissue [187]. To address the possible influence

host life history traits may have on nutritive content, carpenter ants (tribe: camponotini), in

particular, exhibit trophic symbioses with aphids, trading protection from predators for the

honeydew the phloem-feeding insect excretes. Honeydew, as a result, is often a main constituent

of the carpenter ant diets; interestingly, honeydew has been noted for its high asparagine content,

as well as its high provisions of sucrose [188]. Ultimately, these host-niche features, confirmed

through published literature and the data presented here, represent possible targets for

subsequent research seeking to further-illuminate the details of this host-parasite relationship.

Although more work needs to be done, these assays and the lens through which they were applied

stand as a testament to the utility in capitalizing upon the ecophysiological principles governing

organismal survival. Until the year 1959, the publication year of Robert H. Whittaker’s On the

Broad Classification of Organisms, the classification of fungi as part of the Kingdom Plantae was,

as of yet, uncontested [189]. This is a simple example of the toll that using inappropriate tools can

have across many fields of study. The argument made in this work is that, in attempting to better

understand organisms, the best tools available are the principles defining the very forces from

which they result, or, as R. Buckminster Fuller more-eloquently advised, “don’t fight forces, use

them”.

3.2 Conservation of Biological Interfaces and Broader Impacts.

Biological interfaces can be observed across organisms and are reflected in the

architectures of their respective organ systems, tissues and cells [190]. These morphologies, the

most common metric used in delineating barriers of exchange, are selected for as a result of the

inherent physical properties and associated physiological utilities [191], [192]. These so-called

utilities are contingent upon the physical principles governing matter, and it is this mathematical

“inequality” that provides potential energy by way of dimensionality and, therein, the possibility of

chemical gradients [193]. As a result, the relationship between individuals and their environments,

as well as how they adapt to improve the efficiency of those relationships (i.e., exchange), is one

of the most highly conserved, copied and repeated strategies in nature [194]–[196]. This

improvement of organism-environment interaction can be observed in everything from plant root

systems and neurons to even that of fungal hyphae, the latter being the primary focus of this work

[197], [198]. Instances of organism-environment exchange optimization may serve a variety of

physiological functions, including nutrient acquisition and communication, although, homeostasis

is frequently the primary utility [199], [200]. However, some instances of organism-environment

interaction are not characterizable in such simplicity, and, often, this is due to the environment of

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the organism being biotic instead of abiotic, creating a new paradigm of interaction, one involving

multiple organisms [201], [202]. Even when this is the case, however, interface morphology, and

even the mechanisms used in their formation, demonstrate equivalences and homologies with

more traditional organism-environment interactions [196], [200], [203]. Biological interfaces

formed by multiple-organismal contact, emersion, or infiltration are frequently characterized by

their gradients of exchange and the physiological side effects observed in the organisms involved;

for example, it is the observation of these dynamics which facilitates determination of

pathogenesis [204]–[207]. Interactions drawn from these interfaces can be either positive,

amplifying the proficiency of both organisms, or selectively positive-oppositional, amplifying one

organism’s physiology over the others involved in the interaction; however, higher degrees of

complexity may exist as a result of these interfaces.

The most well-known contexts of biological interfaces involving exchange between

multiple organisms can be described as either mutualistic or parasitic symbioses. Examples of

these interfaces range from arbuscular mycorrhizal fungi and plant pathogens to those of

entomopathogenic fungi, such as those discussed here. This work was aimed to highlight and

subvert the shared features characteristic of biological interfaces to examine a complex host and

parasite relationship. Other publications have put-forth this same advantage conferred by these

homologies, particularly when examining host-parasite interactions [208]. In these pursuits,

various ‘omics’ technologies have been utilized, alone and in-combination, to quantify and

characterize these interactions across a large variety of fields and systems. A notable field utilizing

this lens and relevant methods includes cancer research and studies examining pathogens that

generate attention through contribution to the global health burden [209], [210]. Examples of these

pathogens include Mycobacterium tuberculosis (dx: Tuberculosis) and Cryptosporidium parvum

(dx: Cryptosporidiosis) [211]–[214]. Exometabolomics, in particular, has been highlighted among

‘omics’ methods for its utility in understanding organismal “inputs” and “outputs” [215]–[220]. The

use of this method has been used to better understand how microbes change their environments,

and, further, how they might be characterized upon this same quality [12], [98], [221]. This

technique has proved particularly fertile ground for agricultural applications, ranging from

biocontrol and soil remediation to food product quality control and detection of microbial toxins as

contaminants [222]–[225]. It is through use of this technique that novel antibiotics have been

identified and targeted for pharmaceutical development [161]. Particularly relevant, host-parasite

interactions, specifically in case of the malaria-causative agent, Plasmodium falciparum, have

been defined using similar exometabolomic approaches [208], [226].

90

3.3 Future Work.

The desire to understand an organism within its natural context is a logical motive based upon

sound science targeting truth and relevance in any derived ontology; however, it is an ideal that

is, even today, largely unattained. Due to the inherent complexity of biological systems, entire

fields of study (e.g., systems biology, integrative physiology) have been birthed out of the attempts

to study and properly emulate them within a controlled, laboratory environment [96], [227]–[229].

Medical studies using animal models, in particular, have drawn a large amount of criticism

regarding their ontological translatability [227], [230], [231]. Greek and Menache (2013) stated

that, “predicting intra-complex system response is difficult and predicting inter-complex system

response is essentially impossible at higher levels of organization” [232]. Similar to the way in

which this publication challenged the problematic axioms underlying common practices, the

methods broadly applied to the study of specialized parasites within laboratory conditions so-too

demand disruption for the sake of advancement. The parasitic environment is not a simple one,

despite fundamental similarities to other biological systems, and should be treated as such for the

determination of biologically relevant characterizations in laboratory conditions.

Literature regarding the ecology and ecophysiology of fungi is in deficit as it concerns the

inclusion of host-niche and the ecological context of interacting hosts, especially for fungi

demonstrating life cycles requiring or facultatively incorporating multiple hosts. As a result, it is

understandable as to why research continues to bifurcate away from a more holistic

understanding of these systems, particularly when considering in vitro cultivation. It was only in

decades following the 1951 publication of Physiology of the Fungi by Lilly and Barnett [233]28, that

the value of fungal ecology, ecophysiology, and their synthesis began to gain traction in the

scientific community. To-date, and more-so in recent years, much emphasis has been placed on

the potential fungi have as tools of biological control and even the toxicological remediation of

landscapes [49], [234]–[236]. This has further-biased the literature away from ecophysiologically-

relevant contexts, and even recent attempts to re-center the focus of understanding, particularly

as it regards EPFs, were primarily motivated by the host-organisms and not the ecophysiological

context of the fungi involved [63]. Hence, it is becoming imperative that the experimental design,

and the tools developed and implemented, focus on preserving and/or emulating the original

contexts of the organisms that they are used to study.

28 This seminal work was the first of its kind, a comprehensive synthesis of literature assembled for the advancement of fungal physiology, and contributed many key observations for their general study, for example, “there is no universal set of conditions which leads to fructification in all fungi.”

91

Here, it was demonstrated that the use of targeted exometabolomics was immensely

effective in examining the ways in which a complex fungal parasite interacts with its substrata, of

which represented a simulation of the host environment; however, these assays were only

beginning to tap the potential of what this and similar techniques could deliver to the field of host-

parasite interactions. Even though the future of exometabolomics is promising, in regard to this

system in particular, much more work will be required to fully-realize the true nature of this host-

parasite relationship [237], [238]. This research may have illuminated a small portion of the

parasite-side of this relationship, but additional methods will be necessary to examine the ex- and

in vivo dynamics of the infection process, progression and climax, all while maintaining the

system’s biological and ecological context. In light of their combined use in pharmaceutical and

molecular biology labs for high throughput analytics of similarly complex dynamics and

interactions, metabolic fingerprinting, in addition to footprinting (of which was used here without

its complement technique), would benefit the study of this system immensely [239]. The biological

layers presented by the system also impose obstacles; these may be overcome with the

implementation of advanced fluxomics, which has already enabled metabolic phenotyping in,

both, whole multicellular organisms and diverse communities of heterotrophic microbes [240]. The

technologies and computational approaches surrounding this technique are continuously

improving, making such applications ever-increasingly within reach, user-friendly and

economically effective [241]. Use of these or similar methods would allow for simplified

phenotype-genotype link generation, whether it is used within and without the respective host

organism [242]. Being flexible enough for either in- or ex vivo use, it could provide a level of insight

paramount for the advancement of this and similar systems.

Because of the relatively small size of the host organism in this system, biopsies, vital

sampling/measurement and in vivo imaging range from difficult to impossible, making the study

of host-pathogen dynamics throughout pathogenesis challenging. Recent improvements of high-

resolution magnetic resonance (MRI) and mass spectral imaging (MSI) techniques may provide

answers the challenges of working with such difficult host specimens [243]. MALDI-ToF and ToF-

SIMS, although techniques requiring ex vivo sampling processes, are already being used for the

parsing of various, diffuse organismal relationships [244], [245]. In addition to its growing value

as a novel method of culture-independent fungal strain characterization, MSI techniques (i.e.,

MALDI and its variants) allow for high-resolution visualization of molecules across biological

samples [246], [247]. Inorganic ions, which are unobservable with the use of LC-MS, can be

visualized across tissues through utilization of XRF (x-ray fluorescence)[248]–[250]. The

colocalization of pathway components, such as the enzymes, substrates, products and cofactors,

92

may be achieved with the co-administration of these techniques to individual samples. With this

understanding and through a combination of these discussed techniques, pathways of potential

activation could be determined as a result of examining the presence-absence of trace ions

across tissue sections. This method in particular can even be integrated into various forms of

computed tomography (CT) [251]. Use of micro-/nanoCT imaging, alone or in combination with

radioisotopic labeling, would allow for in vivo imaging of the infection process from start to finish,

despite small size of the host. Additionally, certain labeling techniques may be used to target

specific tissues, even enabling for distinction of non-host tissues in-/ex vivo [252]. Due to the

length of incubation period in this system and the extent to which it resembles a closed system,

use of both MSI (i.e., MALDI-ToF, ToF-SIMS) and micro-/nanoCT imaging techniques could offer

the novel opportunity for model development, facilitating more representative teleological

architectures of pathogenesis and within-host dynamics. Lastly, elucidation of exact biochemical

characters delineating the host-parasite interface could easily be performed with the use of

traditional transgenic or CRISPR-enabled GFP-labeling techniques [253].

Ideally, these and other methods could be combined in a holistic fashion to improve the

ecological model detailed within this this work. Use of more advanced techniques designed for

high-throughput sampling and characterization will improve the outlook for this and similar

systems as it regards their examination and translatability. With the ongoing and forthcoming

global change, the future of human health is uncertain, particularly as it concerns our relationships

with microbes. It is experimental systems facilitating low-cost, high-throughput research of

complex pathogens that will better enable our scientific and public health professionals in the face

of a future defined by unpredictable obstacles that will challenge us across scales, cultures and

environments.

93

3.4 Featured Figures.

Figure 3.0 Model Amendment ‒ Proposed Host-niche and Host-parasite Exchange.

94

Figure 3.0 Model Amendment ‒ Proposed Host-niche and Host-parasite Exchange. The original ecological model discussed in Chapter 1 was revised, here, to illustrate how one might include

the revelations or novel hypotheses brought about from this work. This figure shows much of the tested

abiotic influences that represent putative facets of the host-niche, as it relates to the ecological model for

this parasitoid fungus. The broad implications of the results discussed in this work are placed between the

two spaces comprising the host landscape.

95

Figure 3.1 Ecological Model Amendment (full).

96

Figure 3.1 Ecological Model Amendment (full). Similarly to what was depicted in Chapter 1, an ecological model for each species examined in the featured

longitudinal study is represented with alterations (if applicable) to better reflect the hypothetical realized and

fundamental niches of each as supported by the data and respective inferences made in this work. One

posited difference emphasized, here, is the differing incidences of infection (as a result of seasonality) and

the projected effects that this may have had on their respective ecological roles. It should be noted that,

although it has not been confirmed, the incubation period of the two species is assumed differential as a

result of differing levels of virulence; however, it is unknown as to whether this may also have an impact

upon the distance from the host-colony traveled by the infected during the course of manipulation.

97

APPENDIX A: Supplemental Results.

A.1 Physiological Importance of Trace Metal and Macromineral Ions.

A.1.1 Supplementation of Trace Metal and Macrominerals Ions. The gross results of the trace metal and macromineral ion supplementation cultures, firstly, and

as anticipated, recapitulate the substantial impact that inoculation has upon the substrata’s

composition. Differential pH and secreted protein measures also demonstrated blatant distinction

between the various ion supplementations (supplemental figures, B2.3b and B2.3c).

Supplementation of physiologically-relevant concentrations of magnesium, manganese and

calcium ions exhibited highly-similar exometabolomic signatures and were clustered accordingly

within the columnar cut-groups. The supplementation of potassium (again, at a physiologically-

relevant concentration) produced an exometabolomic signature that is nearly indistinguishable

from the inoculated controls (supplemental figure, B2.3a). Iron was supplemented at two separate

concentrations which demarcate the lower and upper limits of the ion’s typical nutritional

requirement range for fungi (i.e., 1 µM and 3 µM). The low-end concentration of ferrous iron was

largely identical in its exometabolomic signature to that of the inoculated controls, possessing

very minor and disparately distributed differences. The high-end concentration of iron

supplementation exhibited an exometabolomic signature more-akin to those of the copper-

supplemented cultures, many features of which markedly contrast with that of the low-end

concentration iron supplementation (i.e., increased detection of acetyl-aspartate, N-acetyl-L-

alanine, 2-ketoisovalerate, L-arginosuccinate; decreased detection of various other metabolites)

and others which parallel with zinc-supplementation (i.e., relative-increased detection of uracil;

relative-decreased detection of D-sedoheptulose-1/7-phosphate and shikimate-3-phosphate).

Two distinct concentrations of copper, one based on general fungal nutritional

requirements and the other of a lesser physiological-relevance, demonstrate characteristic

exometabolomic signatures that cluster within those of the manganese, magnesium, calcium, re-

stabilized-cultivation zinc and simultaneously straddled the high-concentration iron

supplementation within the same subset of the columnar dendrogram (supplemental figure,

B2.3a). Despite their colocalization, these two cultures supplemented with distinct copper

concentrations exhibit blatant differences. The high-concentration copper-supplemented

exometabolomic signature is characterized primarily by the breadth of analytes shown to have

relative-decreased detection, several blocks of these analytes providing distinction from that of

the low-concentration copper-supplementation. Similarly to the copper supplementation cultures,

there are also two distinct experimental cultures supplemented with zinc that were utilized in these

98

experiments; contrastingly, in the case of these cultures, the concentrations were identical. The

two presented exometabolomic signatures possess highly differential characteristics, despite the

similarities of their respective treatments. One culture received blastoconidia which had been

incubated in unrefreshed media for a period of time prior to inoculation, which likely facilitated a

differential response to the supplementation. The late-cultivation zinc-supplemented

exometabolomic signature is the most distinct of any presented in supplemental figure B2.3a,

demonstrating the widest variety of differentially detected metabolites. Turning attention to the re-

stabilized zinc-supplemented culture (after more than 1 re-dilution of blastoconidia into fresh

media), distinction can immediately be curated when comparing to, both, the previously described

zinc-supplemented culture, and the exometabolomic signatures of the other cultures.

Supplementation of macromineral ions (i.e., potassium, magnesium and calcium), in addition to

manganese, failed to demonstrate substantially-distinct profiles relative to control data. No

cultures at end-point, across supplementation conditions, demonstrated any indication that trace

ions or macrominerals exercised a negative impact upon growth.

Differential pH and secreted protein were also determined for these culture

supplementation regimens (supplemental figures, B2.3b and B2.3c). Late-cultivation zinc-

supplemented conditions exhibited the greatest differential pH at end-point relative to controls of

all tested trace ions, concentrations and series (reflective of age as a result of dilutions of

blastoconidia into fresh rich media). Of all ions tested, quantitation of secreted protein relative to

that of control cultivations revealed a gross trend suggesting that supplementation of trace metal

ions (i.e., zinc, copper, iron) was associated with approximately “normal” amounts of secreted

protein, whereas supplementation of macrominerals (i.e., potassium, magnesium, calcium) was

associated with decreased secreted protein. Interestingly, exceptions to these trends were

primarily those of both concentrations of copper and the non-physiologically-relevant

concentration of calcium. The physiologically-relevant concentration of copper demonstrated

decreased secreted protein compared to other trace metal ions tested, the data of this regimen

demonstrating a much greater likeness to those described as “macrominerals”. Contrastingly, its

non-physiologically-relevant concentration (4 µM, copper) exhibited secreted protein

concentrations more akin to the ion’s compatriot “trace metals”. A similar swapping-of-trend was

demonstrated by secreted protein attributed to concentrations of calcium; however, this swap

differed in the fact that the expected trend (macrominerals demonstrating lower relative secreted

protein than those of the trace metal ions) was only disrupted by the non-physiologically-relevant

concentration, which possessed secreted protein concentrations more similarly to those

demonstrated by trace metal ions.

99

A.1.2 Ion Chelation and Titrations. The gross trends visualized in supplemental figure B2.3d illustrated that, in incidences of cell

death, the exometabolomic signatures conferred by each chelator are distinct. This behavior was

reflected in this visualization of the data, resulting in a bifurcated columnar clustering of these

“dead”/”no growth” experimental conditions, orientating bilaterally to the co-clustered and

“live”/”living”-indexed experimental conditions. Those observed as being proliferative or viable at

experimental completion were limited to the lowest titration concentration of TPEN (0.0625 µM)

and all of the titration concentrations employed in the examination of EDTA (200 µM; 20 µM; 2

µM; 0.2 µM). The higher concentrations used for EDTA (2 mM), however, all failed to demonstrate

any viability. The exometabolomic signatures of the EDTA titration samples plainly exhibit a dose-

dependent effect of the chelator upon substrata dynamics. Trends observed within the dead

culture samples were much more polarized and exhibited a more limited scope of differences.

Just as before, pH and secreted protein were determined and compared between

treatment groups relative to an inoculated control (supplemental figures, B2.3e and B2.3f). Across

EDTA supplementation regimens, ∆pH appeared somewhat dose-dependent, the pH changing in

the negative direction with increased concentration of the chelating agent; however, the only

exception to this trend, 200 µM, demonstrated an increased ∆pH relative to control when the trend

would suggest otherwise. A similar but opposite trend in ∆pH observed across titrated

administrations of TPEN (increasing ∆pH with increasing concentrations of the chelator) was also

very similarly disrupted by the second-to-largest concentration of the chelating agent (1 µM),

which exhibited a decreased ∆pH instead of an intermediate magnitude of relative increase. With

the exception of one case (TPEN, 0.0625 µM), all regimens featuring the sole-supplementation

of one of two ion-chelating agents demonstrated decreased secreted protein relative to controls.

Titration of EDTA exhibited a slight dose-dependent effect, secreted protein decreasing even

further with the highest of four tested concentrations. However, this dose dependence is not held

for the late-cultivation culture regimen, which was a much higher amount of the same chelator.

This likely was reflective of the amount of growth, and, therein, protein excretion, capable of being

sustained prior to apoptosis, as all blastoconidia participant in the EDTA titration survived;

however, further examination of cellular impacts of the chelator should be further examined.

100

APPENDIX B: Supplemental Tables and Figures.

B2.0 Experimental Designs.

101

102

B2.1 End-point Trace Ion and Macromineral Studies ‒ Hexmap Index Key.

103

B2.2a-e End-point Exometabolomic Trace Ion/Macromineral Hexmap

Topologies.

a b

c d

e

104

B2.2a-e End-point Exometabolomic Trace Ion/Macromineral Hexmap

Topologies. (a) This figure is representative of a cluster base map derived from the sMap object generated from the

complete input dataset for all end-point exometabolomic assays involving supplementation of trace

metal/macromineral ions, as well as titration and chelation (either of two chelating agents) with or without

rescue through combined administration with a single ion-supplemented in an equimolar fashion. The final

maps generated can be examined in Figure 2.3a. This topology shown illustrates the 6 metacluster bases

determined through application of the self-organizing map algorithm to the input dataset. (b) This figure

shows the number of “hits” or vectors (in this case, these vectors are individual metabolites) assigned in

high-dimensional input space that are reflected for each of the nodes (hexagonal cells) within the generated

hexmaps (2-dimensional output space). Size of each node is scaled accordingly. (c) This figure represents

the distance of a cell’s contents (metabolites identities within the high-dimensional input space of the node)

to its neighbors designated in the 2-dimensional visual space. The higher the density of metabolites per

node/hexagonal cell, the smaller the size of the hexagonal cell featured, here. (d) Here, the metaclusters

determined through the suprahexagonal grid mapping process are represented with their assigned color

and number (1-4). (e) This figure shows the hexmap sBase key indices (each index number has assigned

metabolites; see supplementary table B2.1). All hexagonal topologies were generated using the supraHex

package in R (Bioconductor).

105

B2.3a End-point Exometabolomic Signatures – Trace Ion/Macromineral

Supplementation.

106

B2.3b-c End-point Trace Ion Supplementation – Differential pH and Secreted

Protein.

b

c

107

B2.3d End-point Exometabolomic Signatures – Trace Ion Chelation/Titration.

108

B2.3e-f End-point Trace Ion Chelation/Titration – Differential pH and Secreted

Protein.

e

f

109

B2.3g End-point Exometabolomic Signatures – Trace Ion Rescues.

110

B2.3h-i End-point Trace Ion Rescues – Differential pH and Secreted Protein.

h

i

111

B2.3a-I End-point Trace Ion/Macromineral Supplementation/Chelation/

Rescues. (a, d and g) End-point Trace Ion and Macromineral Supplementation / Chelation and Titration /

Phenotype Rescues. These heatmaps exhibit log2-transformed peak area data of all end-point assays that

featured only supplementation of trace metal/macromineral ions into the original Grace’s Insect Medium

formulation prior to inoculation (or those grouped within the chelation/titration or phenotype rescues, per

respective heatmap). Transformations were performed relative to each respective series’ inoculated control

data, but a non-inoculated control is also shown. All cultures were cultivated under identical conditions for

11 dpi and all were inoculated with an amount of blastospore solution. All data were RSD-filtered (< 25%)

prior to figure generation. Each experimental condition (represented on the vertical, x-axis) label includes

the series number from which it was retrieved, adjacent and in parentheses. The dendrogram parental

groups (colored bar, left x-axis) were manually assigned according to their demonstrated phenotype at end-

point (11 dpi) (green = demonstrating pigmented growth; blue = “dead” or “no growth”; red = “living” or

demonstrating normal growth). Metabolites are shown on the y-axis (top, horizontal). All clustering was

performed using Euclidean distance and Ward linkage methods. For all heatmaps and figures described in

this legend, the biological replicate is n=1; however, all exometabolomic data were acquired in technical

triplicate per experimental condition. (b, e and h) End-point Trace Ion Supplementation / Chelation and

Titration / Phenotype Rescues, Differential pH. This figure complements the preceding figure, and

represents the respective culture conditions’ differential pHs measured at end-point (11 dpi). Differential pH

(∆pH) is shown on the y-axis (left), while the x-axis (bottom) represents the experimental conditions. (c, f

and i) End-point Trace Ion Supplementation / Chelation and Titration / Phenotype Rescues, Secreted

Protein. Here, the secreted protein of these same experimental conditions represented in (a/d/g) and

(b/e/h) is shown; the y-axis (left) reflects the log2-fold difference relative to the control value (µg/mL). The

x-axis (bottom) represents the experimental conditions measured. The legend indicates the source of the

data exhibited in the figure, each of which are reflective of sample analyzed in terms of its dilution from the

original sample and, the third, representative of their mean (■14.29% sample concentration; ■10% sample

concentration; and the averaged value derived from each □, error bars indicate the +/- standard deviation

of averaged data). For protocols, see Methods and Materials (Chapter 2). All heatmaps were generated

using the advanced heatmap function of the supraHex package in R (Bioconductor). For all figures

exhibiting differential pH or relative secreted protein: asterisks (*) adjacent to ion labels indicate ‘non-

physiologically-relevant concentrations’; for copper, this concentration was 4 µM, and, for calcium, this was

1 µM.

112

B2.4 Hexagonal Map with Select Labels – Infraspecific Comparisons.

113

B2.4 Hexagonal Map with Select Labels ‒ Infraspecific Comparisons. This figure provides a simplified graphical representation of the node assignments applicable to the

infraspecific comparison data and hexagonal maps (metaprints and metatracks). This topology illustrates

select analytes that are frequently visited in exposition of the results related to the compared metaprints

and was generated to facilitate reference of cell contents, although the full map index is also provided in

table form (supplemental table B2.6). Hexmap was generated using the supraHex package in R

(Bioconductor); all labels were added manually.

114

B2.5a-e Longitudinal Exometabolomic Hexmap Topologies.

a b

c

e

d

115

B2.5a-e Hexagonally Mapped Exometabolomic Data. All of the featured figures discussed here are reflective of the training dataset (longitudinal exometabolomics

data for O. kimflemingae) and the codebooks applicable to the infraspecific comparison results discussed

in Chapter 2. Figures (a-c) and (e) are informative regarding the input data, while (d) represents the

generated suprahexagonal data. (a) Base Topology. This figure illustrates the bases delineated by the

sMap object of the input training data. Four distinct cluster bases were produced (1-4). (b) Hits Topology.

This figure shows the “hits” per node or number of assigned contents (metabolite vectors) per hexagonal

cell. (c) Distribution Topology. This figure reflects between-neighbor distance in suprahexagonal space

in terms of relative cell-size. (d) Metacluster Topology. This fourth map illustrates the suprahexagonal

metacluster bases generated through the training process and reflects groups based off of their respective

behaviors/traits. (e) Index Topology. This figure represents a map key of node indices to accompany the

visualizations for cell content reference (see supplemental table B2.6). All hexagonal topologies were

generated using the supraHex package in R.

116

B2.6 Hexagonally Mapped Exometabolomic Data ‒ Hexmap Index Key.

117

118

B2.7a-b Asparagine and Methionine ‒ Differential pH and Secreted Protein.

a

b

119

B2.7c-d Dibutyryl-cAMP ‒ Differential pH and Secreted Protein.

c

d

120

B2.7e-f Adjusted Starting pH ‒ Differential pH and Secreted Protein.

e

f

121

B2.7g-h Minimal Medias ‒ Differential pH and Secreted Protein.

g

h

122

B2.7a-h Differential pH and Relative Secreted Protein. For figures (a, c, e, and g), the y-axis (left) denotes the differential pH (∆pH) relative to non-inoculated

control pH, while the x-axis (bottom) denotes the experimental condition measured at end-point (11 dpi);

however, for figure (h), these values are relative to each formulation’s respective non-inoculated control

pH. Additionally, each measurement for this figure (h) were normalized by their respective starting pH

measures. Figure (e), which represents the culture conditions intended to examine the effects of different

starting media pHs, also possesses a second y-axis (right; pH scale) to also show the starting and ending

pH measurements for each respective ∆pH per experimental condition (x-axis). The starting pH values are

shown by the dark gray line (marks = dark gray outline, open circle), while the end-point pH values are

represented by the black line (marks = black outline, open circle). For the final series examining the minimal

media formulations (g-h), three bars are featured: one (■) representative of the differential pH (∆pH) from

each formulation’s respective non-inoculated control, the second (■) representative of the ∆pH resulting

from subtraction of the corrected original media formulation differential pH from the end-point measures of

each experimental condition, and a third (□) representing the ∆pH derived by the subtraction of the non-

inoculated end-point measures corrected by ∆pHO (correction differential; ∆pH of Grace’s Insect Medium

original formulation) from the end-point experimental measures. For figures (b, d, f, and h) depicting the

secreted protein, the y-axis (left) indicates the log2-fold change in value relative to that of the inoculated

control (µg/mL), while the x-axis (bottom) represents the experimental condition that was measured. The

three distinct bars (one black, one gray, and one white with black outline) represent the two concentrations

of the original sample analyzed, and their respective, calculated mean (■14.29% original sample

concentration; ■10% original sample concentration; and their combined mean □, error bars of which

indicate the +/- standard deviation of the averaged data).

123

B2.8 Morphological Changes Between Various Perturbations.

124

B2.8 Morphological Changes Between Various Perturbations. All images were taken using a specialized microscope-adapted digital camera (adapter magnification, 0.5x).

Scale bar was thickened and set in front of a black background for emphasis from original images; scale

bar length still identical to that which is shown in the bottom right-hand corner of each photo. All images

taken were done so under 20x magnification. Image was minimally-edited using Microsoft Office

PowerPoint (-50% Contrast; +20% Brightness; 0% Saturation; +25% Sharpness). Each image is one from

a collection taken for each experimental condition and were chosen on the basis of morphology for

facilitating comparisons. Experimental conditions featured are as follows: (a) 2x Asparagine; (b) 2x

Methionine; (c) Control; (d) Dibutyryl-cAMP 1.0 mM; (e) Dibutyryl-cAMP 1.5 mM; (f) Dibutyryl-cAMP 1.7

mM; (g) starting pH of 4.2; (h) starting pH of 5.0; (i) starting pH of 6.6.

125

B3.0 Traditional SEIR Model and Adjusted Model for a Parasitoid.

126

B3.0 Traditional SEIR Model and Adjusted Model for a Parasitoid. This figure was included as both a visual aid and a proposed model for facilitating future study. It was

intended to complement the ecological models also featured within this work. Here, a traditional SEIR

(susceptible-exposed-infectious-recovered) compartmental model, used for modeling population dynamics

observed with applicable infectious diseases, is juxtapositioned with an additional, hypothetical model that

was drafted in attempt to clarify the unique dynamics presented by a parasitoid, much like the organisms

examined in this work.

127

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