A Holobiont Characterization of Reproduction in a Live-Bearing

Home , Blattabacterium, Cockroach racing, Diploptera, Diploptera punctata, Oviparity, Ovoviviparity

A holobiont characterization of reproduction in a

live-bearing cockroach, Diploptera punctata

A dissertation submitted to the

Graduate School of the

University of Cincinnati

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the Department of Biological Sciences

of the McMicken College of Arts and Sciences

Emily C. Jennings

B.S. Neurobiology, University of Cincinnati, April 2013

Committee Chair: Joshua B. Benoit, Ph.D.

Abstract

Viviparous reproduction is characterized by maternal retention of developing offspring within the reproductive tract during gestation, culminating in live birth. In some cases, a mother will provide nutrition beyond that present in the yolk; this is known as matrotrophic viviparity.

While this phenomenon is best associated with mammals, it is observed in insects such as the viviparous cockroach, Diploptera punctata. Female D. punctata carry developing embryos in the brood sac, a reproductive organ that acts as both a uterus and placenta by protecting and providing a nutritive secretion to the intrauterine developing progeny. While the basic physiology and hormonal changes of D. punctata pregnancy have been characterized, little else is known about this phenomenon. This study attempts to broaden the understanding of D. punctata reproduction by using a multi-omics approach to characterize multiple aspects of the holobiome. First, I utilized RNA-seq analysis to characterize molecular changes associated with

D. punctata reproduction and provides the most complete gene set to date for this species. A comparison of four stages of the female reproductive cycle revealed unique gene expression profiles corresponding to each stage. Differentially regulated transcripts of interest include the previously identified family of milk proteins, transcripts associated with juvenile hormone metabolism, and other reproduction-associated transcripts.

I next utilized 16S rRNA gene sequencing to characterize the microbiome of Diploptera punctata during development, from embryonic development to female adulthood. We identified 50 phyla and 121 classes overall and found that mothers and their developing embryos had significantly different microbial communities, with embryos harboring only a single endosymbiont known as Blattabacteria. Our analysis of postnatal development reveals

i that significant amounts of non-Blattabacteria species are not able to colonize newborn D. punctata until melanization in the first instar, after which the microbial community rapidly and dynamically diversifies. This rapid change of the microbial community appears to stabilize after the second juvenile stage with no major changes even through the last molt to adulthood for females.

Lastly, I sought to better understand the role of the cockroach specific endosymbiont

Blattabacteria as the sole member of the D. punctata embryonic microbial community. We leveraged next generation genome sequencing to computationally characterize the metabolic capacity of Blattabacteria isolated from D. punctata. Our analysis produced a 670,273 base pair chromosome with a high degree of similarity to previously sequenced genomes of

Blattabacteria isolated from other cockroach species. Our genome assembly contained 593 putatively protein coding DNA sequences, which included proteins required for nearly all key metabolic processes including DNA replication, transcription, and translation. Additionally, the

D. punctata strain of Blattabacteria also holds the ability to synthesize nearly all 10 essential amino acids and process nitrogenous waste to do so. This suggests that Blattabacteria are likely providing nutritional supplementation to intrauterine developing embryos to accommodate the previously reported lack of methionine and tryptophan in maternally provided nutrition.

Together, the research presented in this dissertation contributes to a more holistic understanding of this human-like reproductive mode in the cockroach Diploptera punctata, broadening our understanding of viviparity throughout the animal kingdom.

Emily C Jennings

2019

iii Acknowledgements

First and foremost, I want to thank my advisor, Dr. Joshua Benoit, for going out on a limb and taking me on as one of two graduate students the same year he started as a new faculty member at UC. He provided constant support and exhibited unfathomable patience as I stumbled through the last six years in his lab. He encouraged me to think not only critically as a scientist but creatively, encouraging me to take risks on wild experiments like modeling fetal alcohol syndrome in cockroaches. When some of those risks resulted in failed experiments Josh was always there to help me bounce back, just as he was always there to casually offer kudos when something did manage to work out. Josh helped keep me on track when I started to lose focus and dealt with my constant questions. He never questioned my decisions to enroll in courses outside of traditional biology curriculum, whether it was a course about birth defects, or an undergraduate level python programming course. When I realized that I was interested in at least trying a career outside of academia after graduating, he never wavered in his support even when I felt like a quitter. Josh was always open to and encouraged discussions about my project, which sometimes turned into fun debates about where the experiment was going or needed to go. What wasn’t always fun was when one of us had to admit we were wrong, but

Josh was a good sport and never gloated too much when I was wrong even though I wasn’t always so kind when he was wrong. It’s been a wonderful six years in the lab, and I know I’ll miss it when I’m gone.

I’d also like to acknowledge and thank Dr. Elke Buschbeck, Dr. Joshua Gross, Dr. Mike

Polak, and Dr. Matthew Weirauch, for being part of this crazy journey as members of my research advisory committee. They have offered invaluable guidance and support, helping me

iv to navigate and focus my disjointed project ideas, overcome the roadblocks of failed experiments and helping to know when it was time to let go of a stalled-out project. Through it all, I’ve always been able to count on them to be in my corner of the ring.

In addition to serving on my advisory committee, Dr. Elke Buschbeck has played an especially important role in this journey. I’d like to thank her for gambling on hiring me, a weird bug fanatic in her neuroscience course, as a work study back in 2011. She has consistently served as a mentor and role model, helping me grow as a scientist. I cannot begin to describe the value of her constant encouragement and support, letting me know when I was too hard on myself but also making sure to ask if I had something better to do when she noticed I was slacking.

I want to extend my gratitude to all of the current and former members of the Benoit lab for their constant support, listening to me whine when I needed to, dealing with me talking to myself at the lab bench or in our shared office space, and keeping things fun in and out of lab. This work would not be possible without the undergraduate students who have been part of our lab throughout the last 6 years. LaVeta Burke, Luke Bernhardt, John Cavanaugh, Jake

Hendershot, Matthew Korthauer, Erica McDonald, and Sophie Shemas have all made contributions to my research, running countless experiments. Working with them has taught me so much about mentoring, communication, and leadership. It has been an honor to have worked with them. Christopher Holmes and Elise Didion deserve special recognition. I cannot thank them enough for being wonderful lab mates and two of my best friends. Without them this process would have been much harder and very lonely.

v I am so grateful for the patience and support from my family over the last six years.

They never complained when I missed countless birthdays and holidays because I was working or studying, instead welcoming me home with open arms every chance they got. My parents and step-parents have been supportive, kind, and forgiving beyond any reasonable expectation.

Without them, my education would have been impossible. They believed in me when I couldn’t believe in myself, pushed me to constantly be better and strive for more than I thought I could.

I would like to recognize the immense support of my running family. Not only have they been there for me when I needed a friend, but they have listened to me talk about all aspects of graduate school ad nauseum (sometimes for 26.2 miles). I am grateful for the unique perspective and advice that each one of them has provided me over the last two years. In addition to coaching me through three full marathons, countless training runs, and smaller races, they have also coached me throughout the last two years of my PhD, a very different kind of marathon.

Samantha Isler deserves more recognition than I can give her here, meeting her dancing in the rain back in high school might be one of the greatest things that has ever happened to me. I cannot imagine my life without her, and I cannot thank her enough for being my best friend for all of these years. She has been there for me through every high and every low, my light in the dark.

Lastly, I need to thank my husband, Justin. He has loved me when I was a nightmare, always understood how much my research means to me, and forgiven me when I put him second to science. He has always supported all of my wildest dreams, even when I changed my mind constantly about what those were. Justin is constantly lifting me up, assuring me that I am

vi enough, and that I deserve to be where I am. I’m fairly certain that I wouldn’t be able to accomplish anything without him, including this degree. He is my best friend, an amazing husband, a wonderful dog and cat dad, my number one fan, and also the most resilient person I know. For all this and more, I am eternally grateful.

vii

In loving memory of Dennis Whitehead

Thank you for letting me write a paper about Rosalind Franklin instead of Watson & Crick,

among the many other things you let me get away with.

viii Table of Contents

Abstract ...... i

Acknowledgements ...... iv

Chapter 1 ...... 2

References ...... 11 Chapter 2 ...... 26

Abstract ...... 27 Introduction ...... 28 Methods ...... 29 Results ...... 34 Discussion ...... 38 References ...... 45 Tables ...... 56 Legends and Figures ...... 67 Chapter 3 ...... 76

Introduction ...... 78 Methods ...... 81 Results ...... 85 Discussion ...... 90 References ...... 98 Tables ...... 115 Figures and legends ...... 121 Chapter 4 ...... 129

Abstract ...... 130 Introduction ...... 131 Methods ...... 133 Results ...... 135 Discussion ...... 136 References ...... 142

Tables ...... 150 Figures and legends ...... 151 Chapter 5 ...... 156

References ...... 168 Appendix ...... 184

Chapter 1

Introduction and Background

2 Overview of matrotrophic viviparity in insects: Within the animal kingdom there exist three main reproductive strategies: oviparity (laying of eggs), facultative viviparity in which a mother may lay eggs or give birth to live, active offspring, and true viviparity (Hagan, 1948; Meier,

Kotrba, & Ferrar, 1999; Roth & Willis, 1957; Stewart, 2015). Truly viviparous reproduction is characterized by the obligate retention of developing progeny in the maternal reproductive tract for the duration of gestation. This process culminates in the birth of active offspring; early termination of the pregnancy results in stillborn offspring (Hagan, 1948; Kalinka, 2015).

Viviparous reproduction can be further divided into two more specific classifications: lecithotrophic viviparity, in which mothers provide only fluids to developing progeny; and matrotrophic viviparity which is characterized by the provisioning of both fluids and non-yolk nutrients to embryos during intra-uterine development (Blackburn, 2014; Blackburn & Starck,

2015). Females that reproduce via true viviparity must carry, provide fluids, and, in the case of matrotrophic viviparity, supply nutrients to their offspring. Thus, this reproductive strategy is associated with an extremely elevated cost for the mother when compared with the investments of oviparity and facultative viviparity (Bleu, Massot, Haussy, & Meylan, 2012;

Hopkins, Eldridge, & Cech, 1995; Schultz, Webb, & Christian, 2008; Shaffer & Formanowicz,

1996).

Despite the immense investments required, viviparity has independently evolved hundreds of times across the animal kingdom (Blackburn, 2014; Crespi, 1989; Hagan, 1948;

Hussey et al., 2010; Kalinka, 2015; Meier et al., 1999; Roth & Willis, 1957; Shine, 1983;

Tworzydlo, Kisiel, & Bilinski, 2013). Lecithotrophic viviparous animals include some squamate lizards and snakes, such as the aspic viper, multiple amphibians as well as fish (Blackburn, 1992;

3 Hagan, 1948; Hussey et al., 2010; Roth & Willis, 1957). Present in at least 13 orders of insects

(Clutton-Brock, 1991; Hagan, 1948; Roth & Willis, 1957), viviparity has at least 62 independent origins within the 22 Dipteran families (Kalinka, 2015; Meier et al., 1999).

Matrotrophic viviparity has widespread representation in the animal kingdom (21 of the

34 animal phyla) (Ostrovsky et al., 2016). Squamate reptiles, such as the three-toed skink; certain anuran amphibians; and teleost fish such as the yellowtail rockfish have documented viviparous species (Blackburn, 1992; Macfarlane & Bowers, 1995). There are 213 families of chordates and 45 arthropod families that have been estimated to be matrotrophic viviparous

(Ostrovsky et al., 2016). However, arthropods at large have the greatest diversity of progeny incubation and matrotrophy in the animal kingdom (Ostrovsky et al., 2016). Matrotrophic viviparity has been observed in many insects such as dermapterans, multiple fly species, most notably the Glossinadae (Attardo et al., 2019; Denlinger & Ma, 1974; Ma, Denlinger, Järlfors, &

Smith, 1975; Tobe & Langley, 1978), and the Pacific beetle mimic cockroach, Diploptera punctata (Hagan, 1939; Roth & Stay, 1961; Roth & Willis, 1955; Stay & Coop, 1974; Stay & Roth,

1956). Despite the prevalence of matrotrophic viviparity in insects, little is known about the molecular underpinnings of this phenomenon outside of tsetse flies (Attardo et al., 2019;

Benoit et al., 2014; Benoit, Attardo, Baumann, Michalkova, & Aksoy, 2015; Guz, Attardo, Wu, &

Aksoy, 2007; Michalkova, Benoit, Attardo, Medlock, & Aksoy, 2014; Michalkova, Benoit, Weiss,

Attardo, & Aksoy, 2014). Increasing the degree to which this human-like pregnancy is understood in a diverse set of animals will broaden the lens through which researchers view the evolution of matrotrophic viviparity.

Overview of microbiomes and maternal transmission: It has become common knowledge that animals share their bodies to host a diverse suite of microorganisms known as the microbiome

(Engel & Moran, 2013; The NIH HMP Working Group, 2009). The microbiome has been found to play important roles in a variety of host processes, such as nutrient metabolism to immunity

(Albenberg & Wu, 2014; Chung et al., 2012; Dimmitt et al., 2010; Douglas, 2017; Jašarević,

Rodgers, & Bale, 2015; Michalkova, Benoit, Weiss, et al., 2014; Pais, Lohs, Wu, Wang, & Aksoy,

2008; Snyder & Rio, 2015; Wang, Weiss, & Aksoy, 2013; Weiss, Wang, & Aksoy, 2011). Most insects reproduce by oviparity and consequently acquire the majority of diverse microbial symbionts in their microbiome over the course of development (Abdul Rahman et al., 2015;

Bright & Bulgheresi, 2010; da Costa & Poulsen, 2018; Estes et al., 2013; Funkhouser &

Bordenstein, 2013; Salem, Florez, Gerardo, & Kaltenpoth, 2015; Schwab, Riggs, Newton, &

Moczek, 2016; Shukla, Vogel, Heckel, Vilcinskas, & Kaltenpoth, 2018). In some cases, symbionts can be passed from oviparous mothers to their offspring before birth by incorporating the microbes into the developing eggs (Abdul Rahman et al., 2015; Binkhorst et al., 2012; Estes et al., 2013; Schwab et al., 2016; Shukla et al., 2018). However, the extensive and complex interactions between mother and offspring during pregnancy in matrotrophic viviparous insects provide an opportunity for mothers to vertically transmit microbes to their developing offspring

(Denlinger & Ma, 1975; Funkhouser & Bordenstein, 2013; Morse et al., 2013; Wang et al.,

2013). However, little is known about the role that insect viviparous reproduction plays in microbial transmission outside of tsetse flies, which are able to transfer important symbiotic bacteria to internally developing offspring through nutritive milk-like secretions (Bing et al.,

5 2017; Cheng et al., 2000; De Vooght, Caljon, Van Hees, & Van Den Abbeele, 2015; Douglas,

2017; Snyder, Mclain, & Rio, 2012; Snyder & Rio, 2015; Wang et al., 2013). Considering the ubiquity of viviparity across the animal kingdom and the degree to which the extended interactions between mother and offspring during gestation can impact offspring, further study of microbial inheritance in live-bearing species is warranted.

Nutritional contributions of endosymbionts: Endosymbionts live inside the tissues and cells of their host and are consistently transmitted from mother to offspring during reproduction

(Moran & Wernegreen, 2000; Wernegreen, 2002, 2004). The stability in environment and transmission allows these bacteria to specialize, co-evolving with their host (Hosokawa, Kikuchi,

Nikoh, Shimada, & Fukatsu, 2006; Kinjo et al., 2018; Moran & Wernegreen, 2000; Neef et al.,

2011; Nikoh, Hosokawa, Oshima, Hattori, & Fukatsu, 2011; Sabree et al., 2012; Wernegreen,

2002; Wilson et al., 2010). This co-evolution of endosymbiotic bacteria with their host yields drastic genome reductions over the course of these increasingly mutualistic relationships, often times seen as near complete elimination of some cellular functions in the genome; typical functions lost include cell wall components, cell motility, response to the environment as well as signal transduction (Akman et al., 2002; Jiang et al., 2013; Kinjo et al., 2018; López-Sánchez et al., 2008; McCutcheon & Moran, 2010, 2012; Nakabachi et al., 2006; Neef et al., 2011;

Santos-Garcia et al., 2014; Snyder et al., 2012; van Ham et al., 2003; Wolf & Koonin, 2013).

Despite the drastic reductions seen in previously characterized endosymbiont genomes, obligate endosymbionts often retain genes encoding complex metabolic pathways like those for amino acid or vitamin biosynthesis (Akman et al., 2002; Michalik, Szklarzewicz, Jankowska, &

6 Wieczorek, 2014; Sabree et al., 2012; Sabree, Kambhampati, & Moran, 2009; Snyder & Rio,

2015; Tokuda et al., 2013; Wilson et al., 2010). Wigglesworthia morsitans, an endosymbiont transmitted via milk secretions in the live-bearing tsetse fly, has lost many genes functioning in regulatory processes and transport related functions in its reduced genome (Akman et al.,

2002) (Akman et al 2002). Despite its small size, the W. morsitans genome has retained the ability to synthesize multiple B-vitamins and cofactors (Akman et al., 2002), metabolites that are required for host fitness but absent in the tsetse fly diet of vertebrate blood (Attardo et al.,

2019; Bing et al., 2017; Michalkova, Benoit, Weiss, et al., 2014; Pais et al., 2008). In phloem feeding aphids, nearly 10% of open reading frames in the genome of its stable endosymbiont,

Buchnera aphidicola, encode the biosynthesis of essential amino acids required by the aphid host (Shigenobu, Watanabe, Hattori, Sakaki, & Ishikawa, 2000; van Ham et al., 2003).

Interestingly, genes encoding non-essential amino acid and cofactor biosynthesis are absent in the B. aphidicola genome meaning that the symbionts must acquire these compounds from the host as many serve as the building blocks for essential amino acid synthesis (Michalik et al.,

2014; van Ham et al., 2003; Wilson et al., 2010).

Cockroaches also have a primary endosymbiont, Blattabacterium (Bandi et al., 1994,

1995; Nalepa, Bignell, & Bandi, 2001). Transmitted transovarially, these bacteria reside in specialized bacteriocyte cells in the fat body organ and near the ovaries (Bandi et al., 1994,

1995; Nalepa et al., 2001). Genomic surveys of multiple strains of Blattabacterium revealed mechanisms underlying the previously observed effect of these bacteria on the storage of nitrogenous waste as uric acid (Hamilton & Schal, 1988) and sulfur assimilation (Block & Henry,

1961; Henry & Block, 1961), in addition to many other metabolic pathways maintained in a

7 reduced genome (Huang, Sabree, & Moran, 2012; Kambhampati, Alleman, & Park, 2013; López-

Sánchez et al., 2008, 2009; Patino-Navarrete et al., 2014; Patiño-Navarrete, Moya, Latorre, &

Peretó, 2013; Sabree et al., 2009; Tokuda et al., 2013). These bacteria allow cockroaches to recycle uric acid to produce important metabolic products such as essential amino acids which may be lacking in the diet of what are commonly detritivores (Huang et al., 2012; Kambhampati et al., 2013; López-Sánchez et al., 2008, 2009; Patino-Navarrete et al., 2014; Patiño-Navarrete et al., 2013; Sabree et al., 2009; Tokuda et al., 2013). These among many other genomic analyses of the relationships between endosymbionts and hosts have revealed likely evolutionary benefits related to diet and niche expansion, but there is still much to be learned about these relationships in contexts such as host reproduction from genomic analyses of endosymbionts.

Diploptera punctata as a model: The beetle mimic cockroach, Diploptera punctata, a Polynesian native species, reproduces by matrotrophic viviparity (Ingram, Stay, & Cain, 1977; Roth & Hahn,

1964; Roth & Stay, 1961; Roth & Willis, 1955; Stay & Coop, 1974). Embryos develop inside the brood sac, a unique organ which functions as both a uterus and pseudo-placenta; embryos are provided with nutrients by a secretion of milk-like components (Roth & Stay, 1961; Roth &

Willis, 1955; Stay & Clark, 1971; Stay & Coop, 1973; Stay & Roth, 1956). This secretion appears in embryo gut contents at 20% of the 60-70-day pregnancy, when the dorsal edge of the body wall is closed. Diploptera milk is a combination of proteins and free amino acids, carbohydrates, and lipids in a water base (Stay & Coop, 1973, 1974; Williford, Stay, & Bhattacharya, 2004). The proteins present include a family of milk proteins derived from 25 unique genes coding for 22

8 different proteins (Stay & Coop, 1973, 1974; Williford et al., 2004). During its gestation period, between nine and thirteen embryos grow from 1.5 mm at the time of ovulation to over 6mm at birth, increasing in weight by more than 70-fold. Water content of each egg increases by 85- fold, and solid dry weight increases over 49-fold (Roth, 1967; Roth & Hahn, 1964; Roth & Stay,

1961; Roth & Willis, 1955; Stay & Coop, 1973, 1974; Stay & Roth, 1956). While the basic physiology of this process has been characterized, studies of molecular and genetic aspects of matrotrophic viviparous reproduction in D. punctata have been limited to early stages in reproduction before ovipositioning into the brood sac (Garside, Koladich, Bendena, & Tobe,

2002; Huang, Hult, Marchal, & Tobe, 2015; Huang et al., 2014; Hult, Huang, Marchal, Lam, &

Tobe, 2015; Liu, Lin, Lin, Yeh, & Chiang, 2005; Marchal et al., 2014; Marchal, Hult, Huang, Stay,

& Tobe, 2013).

Overview of thesis: Matrotrophic viviparity is widespread throughout the animal kingdom and prevalent in the insects. Characterized by the nutrient provisioning to developing offspring within the maternal reproductive tract and culminating in live birth, this type of reproduction is complex and multifaceted. Despite being well represented in the insects, matrotrophic viviparity has not been well characterized at the molecular level outside of the trypanosomiasis vectors in Glossinidae. While matrotrophic viviparity in the Pacific beetle mimic cockroach,

Diploptera punctata, has been well characterized physiologically very little is known about the reproduction of this animal at the molecular level outside the period of oogenesis and ovipositioning. The work presented in this thesis addresses the knowledge gap in the reproductive biology of this cockroach by taking a holobiont approach leveraging next-

9 generation sequencing techniques. First, I aimed to close the gap in knowledge regarding the genetic underpinnings of pregnancy in D. punctata utilizing transcriptomics and gene knockdowns by RNA interference. Second, I characterized the development of the D. punctata microbiome using 16S rRNA gene sequencing to determine if reproduction by matrotrophic viviparity facilitated the prenatal transmission of symbionts. Lastly, I sequenced and characterized the genome of the primary endosymbiont of D. punctata, a strain of

Blattabacterium. This provided key insights into the metabolic capacity of this bacterium and the potential roles that it plays in the development of its host. The culmination of this work characterizing the holobiome of D. punctata provides novel information about matrotrophic viviparity in this species as well as throughout the animal kingdom, thus broadening our understanding of this extraordinary example of convergent evolution.

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Chapter 2

RNA-seq analysis sheds light on the molecular mechanisms underlying

the control of live birth in the cockroach, Diploptera punctata

Emily C. Jennings 1 *

Jacob M. Hendershot1, Matthew T. Weirauch2, Jose M. C. Ribeiro3, and Joshua B. Benoit1

1 Department of Biological Sciences, University of Cincinnati

2 Center for Autoimmune Genomics and Etiology and Divisions of Biomedical Informatics and

Developmental Biology, Cincinnati Children’s Hospital Medical Center

3 Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious

Diseases

* The work presented here was principally composed by Emily C Jennings with assistance

from the listed co-authors.

26 Abstract

Viviparous reproduction is characterized by maternal retention of developing offspring within her reproductive tract during gestation, culminating in live birth. In some cases, a mother will provide nutrition beyond that present in the yolk; this is known as matrotrophic viviparity.

While this phenomenon is best associated with mammals, it is observed in insects such as the viviparous cockroach, Diploptera punctata. Female D. punctata carry developing embryos in the brood sac, a reproductive organ that acts as both a uterus and placenta by protecting and providing a nutritive secretion to the intrauterine developing progeny. While the basic physiology of D. punctata pregnancy has been characterized, little is known about the molecular mechanisms underlying this phenomenon. This study combines RNA-seq analysis,

RNA interference, and other assays to characterize molecular and physiological changes associated with D. punctata reproduction, providing the most complete gene set to date for this species. A comparison of four stages of the female reproductive cycle revealed unique gene expression profiles corresponding to each stage. Differentially regulated transcripts of interest include the previously identified family of milk proteins and transcripts associated with juvenile hormone metabolism. RNA interference experiments reveal potential impacts of juvenile hormone breakdown in maintaining pregnancy in D. punctata.

27 Introduction

Within the animal kingdom there exist three main reproductive strategies: oviparity or the laying of eggs, facultative viviparity (encompassing ovoviviparity to aplacental viviparity) in which a mother may lay eggs or give birth to live, active offspring, and viviparity (Hagan, 1948;

Meier, Kotrba, & Ferrar, 1999; Roth & Willis, 1957; Stewart, 2015). True viviparous reproduction is characterized by the obligate retention of and nutritional provisioning to developing progeny in the maternal reproductive tract for the duration of gestation, which culminates in the birth of active offspring; early termination of the pregnancy results in stillborn offspring (Hagan, 1948; Kalinka, 2015). While most insects are oviparous, facultative or true viviparity has been observed in at least 13 orders of insects (Clutton-Brock, 1991; Hagan, 1948;

Roth & Willis, 1957). One such viviparous insect is the pacific beetle mimic cockroach,

Diploptera punctata (Ingram, Stay, & Cain, 1977; Roth & Hahn, 1964; Roth & Stay, 1961; Roth &

Willis, 1955; Stay & Coop, 1974).

Diploptera punctata, a Polynesian native species, reproduces by matrotrophic viviparity

(Figure 1A and B) (Ingram et al., 1977; Roth & Hahn, 1964; Roth & Stay, 1961; Roth & Willis,

1955; Stay & Coop, 1974). Embryos develop inside the brood sac, a unique organ which functions as both a uterus and pseudo-placenta (Figure 1B); embryos are provided with nutrients by a secretion of milk-like components (Roth & Stay, 1961; Roth & Willis, 1955; Stay &

Clark, 1971; Stay & Coop, 1973; Stay & Roth, 1956). Crystalized milk secretion accumulates in embryo gut contents at 20% of the 60-70-day gestation period, at which point the dorsal edge of the body wall has closed. Diploptera milk is a combination of proteins and free amino acids, carbohydrates, and lipids in a water base (Stay & Coop, 1973, 1974; Williford, Stay, &

28 Bhattacharya, 2004). The proteins present include a family of milk proteins derived from 25 unique mRNAs coding for 22 different proteins (Stay & Coop, 1973, 1974; Williford et al., 2004).

During its gestation period, between nine and thirteen embryos grow from 1.5 mm at ovulation to over 6mm at birth, increasing in weight by more than 70-fold. Water content of each egg increases by 85-fold, and solid dry weight increases over 49-fold (Roth, 1967; Roth & Hahn,

1964; Roth & Stay, 1961; Roth & Willis, 1955; Stay & Coop, 1973, 1974; Stay & Roth, 1956).

While the basic physiology of D. punctata pregnancy has been characterized, little is known about the molecular mechanisms underlying this phenomenon. This study combines RNA-seq analysis, RNA interference, and other assays to characterize molecular changes associated with

D. punctata reproduction. We present a transcriptome containing 11,987 coding DNA sequences, 2,474 of which are differentially regulated across pregnancy in D. punctata.

Additionally, gene knockdown of juvenile hormone esterase indicates a key role for this enzyme in maintaining milk protein production but does not act alone for complete maintenance of pregnancy.

Methods

Animals

Colonies were reared at the University of Cincinnati in a climate-controlled facility. Ambient temperature was held between 24-28°C and relative humidity (RH) was held between 70-80%.

A 12:12 hour light-dark photoperiod was maintained for the duration of the experiment.

Animals were provided water and fed Old Roy Complete Nutrition brand dog food (Mars, Inc.) ad libitum.

Sample collection

Females used in transcriptome analysis are divided into four categories: mated but not pregnant, pre-lactation, early lactation, and late lactation (Table 1). Pregnant females harboring embryos less than 1.6 mm in length are considered pre-lactation; those with embryos between

1.6 and 2.5 mm are early lactation; and those with embryos greater than 2.5 mm are considered late lactation. Lastly, mated but not pregnant females are characterized by the presence of a spermatophore in the bursa copulatrix and absence of embryos in the brood sac.

These parameters are based on those reported by Stay and Coop (1973). Females were randomly chosen from the colony and embryos were carefully dissected out of the brood sac in sterile PBS and immediately measured. Once the samples were categorized, embryos were discarded, and due to their size, females were cut in half at the junction between the thorax and abdomen and stored in Trizol (Invitrogen) at -80°C until processed.

RNA extraction and library preparation

Total RNA was extracted from whole animals or females without developing embryos using

Trizol and then treated with DNase I (Thermo Scientific) to remove genomic DNA. Samples were then treated with a GeneJET RNA Cleanup and Concentration Micro Kit (Thermo Scientific) to further remove contamination. For pregnant female samples, equal parts of RNA extracted from the anterior and posterior were combined before DNase I treatment. Total RNA was pooled from 3 individuals – each extracted individually – for each replicate; a second set of samples was prepared for qPCR validation of select transcripts. In addition, three male samples

30 each consisting of a pool of three individuals and one sample containing equal amounts of RNA from a male, first instar nymph, pregnant female, and non-pregnant female were collected for sequencing. RNA concentration and quality were examined via A260/A280 and A260/A230 with a NanoDrop 2000 (Thermo Scientific).

Poly(A) libraries were prepared by the DNA Sequencing and Genotyping Core at

Cincinnati Children’s Hospital Medical Center. RNA was quantified using a Qubit 3.0

Fluorometer (Life Technologies). Total RNA (150-300 ng) was poly(A) selected for reverse transcription using a TruSeq Stranded mRNA Library Preparation Kit (Illumina). Multiplexing was conducted by ligating an 8-base molecular barcode to sequences before 15 cycles of PCR amplification and HiSeq 2500 (Illumina) Rapid Mode library sequencing. Sequencing resulted in

30-40 million single end strand specific reads per sample at either 75bp or 100 bp long.

Bioinformatic analysis

Initial bioinformatic analyses (e.g. de novo assembly of contigs, identification of coding sequences, and basic characterization of coding sequences) were conducted as previously described (Ribeiro, Genta, et al., 2014; Ribeiro, Chagas, Pham, Lounibos, & Calvo, 2014). Briefly, low quality regions and reads of low quality are removed using RTA 1.12.4.2 (Illumina) and

CASAVA 1.8.2 (Illumina). The remaining high-quality reads are used to generate de novo assemblies with both ABySS software (Birol et al., 2009; Simpson et al., 2009) and the

SOAPdenovo-Trans assembler (Luo et al., 2012), which were joined by an iterative BLAST and cap3 assembler. Coding DNA sequences were determined based on similarity to known proteins or by obtaining CDS containing a signal peptide. Annotation utilized multiple databases

31 including Swissprot, Gene Ontology, KOG, Pfam, and SMART as well as the non-redundant protein database of the National Center for Biotechnology Information. Additional manual annotation was conducted as needed. Further description of this procedure can be found in previous publications (Budachetri & Karim, 2015; Ribeiro, Chagas, et al., 2014). Assessment of differential expression was conducted using the EDGE method with default settings in CLC

Genomics Workbench (QIAGEN) to generate normalized expression values as the number of transcripts per kilobase per million (TPM); false detection rate (FDR) corrected p-values less than 0.05 were considered significant. Differential enrichment of functional annotations was conducted using annotation and Fisher’s Exact test functions in Blast2GO5 Basic (BioBam), and the Gene Set Enrichment Analysis (GSEA) function of CLC Genomics Workbench.

RNA interference

Dicer-substrate short interfering RNAs (siRNA) consisting of two duplex sequences were designed for the D. punctata juvenile hormone esterase transcript (jhe) and green florescent protein (gfp) as a control using the IDT custom siRNA design tool (Integrated DNA Technologies)

(Table 2). The siRNA was reconstituted in nuclease free water according to manufacturer specifications and diluted to 1µg/µl. Pregnant females (status was confirmed by the presence of embryos in the brood sac) were collected from the colony and grouped based on weight.

Cockroaches were injected with 2 µl of either siGFP or siJHE according to the following schedule based on previous studies in D. punctata (Hult, Huang, Marchal, Lam, & Tobe, 2015). The first injection was considered as day 0 and subsequent injections were given on day 2, day 4, day 5,

32 and day 6. Embryos were removed from cold anesthetized females on day 7 and female tissue was stored in Trizol (Invitrogen) at -80° C until RNA was extracted.

cDNA synthesis and quantitative PCR

Extracted RNA was diluted to a concentration of 200 ng/µl for use in reverse transcription reactions. Complimentary DNA (cDNA) was synthesized using a DyNAmo cDNA Synthesis Kid

(Thermo Scientific) from 1µg of RNA for gene specific expression validation and 250 ng of RNA for RNAi knockdown experiments. KiCqStart SYBR Green qPCR ReadyMix (Sigma Aldrich) was utilized in all reactions with gene specific primers designed using Primer3 (Hancock & Zvelebil,

2014) (Table 2). Quantitative PCR was conducted in an Illumina Eco quantitative PCR system; reactions were run according to a previous study (Rosendale, Romick-Rosendale, Watanabe,

Dunlevy, & Benoit, 2016). Relative expression of genes of interest was calculated with the DDCq method (Schmittgen & Livak, 2008) using Elongation factor 1 alpha (Ef1a) for normalization. For

RNA-seq expression validation, fold change in the genes of interest was calculated relative to non-pregnant females and the logarithmic fold change was plotted against the corresponding value from the RNA-seq analysis to calculate a Pearson correlation coefficient (r). Relative mRNA expression levels for each gene of interest, calculated as described for RNA-seq validation, were compared between treatment groups in RStudio (R Core Team, 2017; RStudio

Team, 2015) and were compared between controls and knockdowns using a Wilcox test.

33 Data processing and visualization

Data processing was conducted in Microsoft Excel (v.16.22) and R (v.3.3.3) (R Core

Team, 2017) using RStudio (v1.1.423) (RStudio Team, 2015). Additional statistics and graphical representations of data were also performed in R using RStudio. Packages utilized include dplyr

(Wickham, Francois, Henry, & Müller, 2017), ggplot2 (Wickham, 2016), reshape2 (Wickham,

2007), RColorBrewer (Neuwirth, 2014), Rmisc (Hope, 2013), wesanderson (Ram & Wickham,

2018), and yarrrr (Phillips, 2017).

Results

General assembly characteristics

Quality control processing of the raw sequencing data produced 460,755,803 single end reads which were assembled into 102,880 contigs (Table 3). Representing 10,820,347 bases, 11,987 of those contigs were determined to be coding sequences (Table 3) and 3,289 of them had signatures indicative of signaling peptides (i.e., exported from the cell). BUSCO analysis revealed that 80.5% of the benchmarking universal single copy orthologs were represented as complete genes in our gene set with another 2.3% present as fragments (Kriventseva et al.

2015), indicating that the assembly is of good quality (Figure 2A). Contigs were annotated by searching NCBI’s arthropod non-redundant database and Swissprot using BLASTx; 77.8% of extracted CDS matched a protein in the nr arthropod database with an e-value ≤ 0.001. The termite Zootermopsis nevadensis appeared as the species with the highest similarity for 57.30% of BLAST annotations followed by Tribolium castaneum, representing only 2.85% of BLAST top- hits (Figure 2B).

34 Our analysis revealed 2,474 contigs that are differentially expressed after mating across

D. punctata pregnancy. In these 2,474 contigs, 75 Gene Ontology (GO) terms were overrepresented (Figure 3A). Overrepresented terms included biological processes such as cytoskeleton organization, cell cycle processes, morphogenesis of epithelium, multiple metabolic and biosynthetic processes; cellular components like ribosomes; and molecular functions associated with cuticle components and the binding of protein and carbohydrates.

Each stage of pregnancy had a visibly and statistically unique profile of differentially regulated genes associated with an equally unique sets of GO terms (Table 4). Using an FDR corrected p- value cut off of 0.01 and a minimum absolute value of fold change of 1.5, we detected 163 pre- lactation-specific genes (38 upregulated and 125 downregulated), 414 early lactation-specific genes (123 upregulated and 291 downregulated), and 287 late lactation-specific (41 upregulated and 246 downregulated) (Figure 3B). Pregnant females producing milk secretion show additional enrichment of GO terms for protein metabolic process, cuticle development, transposition, and RNA splicing relative to non-pregnant females while pre lactation females have increased representation of terms for inorganic ion transport, generation of precursor metabolites and energy, cell differentiation, nucleic acid metabolism, and mitochondria related energy production (Table 4).

To identify specific male expressed genes, we selected CDS with five-fold or greater expression in males when compared to both late lactation females and mated but not pregnant females with an FDR p-value cutoff ≤ 0.01. This analysis produced 286 CDS with male specific expression (Figure 4A). When annotated using BLASTx against the Zootermopsis nevadensis nr database subset and arthropod nr database, only 64 of the 286 CDS had blast matches with an

35 e-value ≤ 0.001. GO analysis revealed an enrichment of serine-type endopeptidase activity, serine-type peptidase activity, serine hydrolase activity and general hydrolase and catalytic activities; also enriched were the biological processes for proteolysis and protein metabolic processes (Figure 4B).

Differential expression of transcriptional regulators

We also sought to identify transcription factors that could be associated with or driving reproduction in D. punctata. Bioinformatic analyses identified 329 putative transcription factors in our assembly, using methods previously described (Schoville et al., 2018). These transcription factors belong to 35 structural families, with C2H2 zinc fingers being the most abundant followed by homeodomains, bHLH, bZIP and then Myb domains (Figure 5A). Of the corresponding 329 transcripts, ten are differentially regulated, including krüppel homolog 1 and ftz-f1, which are known to interact with juvenile hormone as well as ecdysone, another important insect hormone (Figure 5B).

Reproduction specific gene expression

Previous research of Diploptera punctata reproduction resulted in the identification of 25 mRNA sequences that appear to encode the proteins that constitute the main nutritional content of the milky secretion provided to intrauterine developing embryos during pregnancy

(Evans & Stay, 1994; Ingram et al., 1977; Stay & Coop, 1974; Williford et al., 2004). These proteins have been shown to increase in concentration across pregnancy (Evans & Stay, 1994;

Ingram et al., 1977; Stay & Coop, 1974; Williford et al., 2004). Using the available mRNA clone

36 sequences from the NCBI database as a reference for mapping our RNA-seq reads, we calculated their expression in the four reproductive stages surveyed. The pattern of expression identified in our analysis combined with the previously conducted mRNA clone sequencing and protein sequence analysis confirms that these sequences encode the milk-proteins and that their transcription mirrors the secretion patterns (Figure 6A). Additionally, we identified and characterized the expression of transcripts for vitellogenin, the main yolk protein precursor

(Figure 6B) and juvenile hormone esterase (jhe), the enzyme primarily responsible for breaking down and suppressing levels of juvenile hormone to allow the maintenance of pregnancy (Rotin

& Tobe, 1983). Both were differentially regulated across pregnancy and as expected inversely mirrored the known titer of juvenile hormone (Figure 6C).

Knockdown of JHE

In our RNA-seq data set we identified a putative transcript encoding JH esterase, which was differentially regulated across female reproduction (Figure 6C). To determine the degree to which the increased expression of the JH esterase transcript contributes to the low JH titer controlling milk production, we utilized short interfering RNA (siRNA) to transiently knock down the expression of JH esterase. We measured relative expression of a milk protein clone MP13Z, ecdysone receptor (EcR) and transcripts known to respond to juvenile hormone levels, vitellogenin (Vg) and krüppel homolog 1 (Krh1). Relative to the siGFP RNAi injected females, the siJHE individuals demonstrated a significant reduction in JHE expression, indicating a successful knockdown of the transcript (Figure 7). There was also a significant decrease in levels of the milk protein clone MP13Z, indicating that JHE contributes to maintaining low circulating levels

37 of juvenile hormone in pregnant D. punctata. However, we did not see a corresponding increase in vitellogenin; rather, the levels of Vg transcripts did not vary between the siGPF controls or the siJHE knockdowns, but both groups had high variability in the expression of this gene. While we had predicted that Krh1 would increase in expression due to an increase in circulating JH, we instead saw a reduction of this transcript in siJHE knockdowns relative to siGFP controls. Additionally, there was no change in the expression of EcR (Figure 7).

Discussion

Utilizing RNA-seq analysis we assembled an 102,880 contig de novo transcriptome of D. punctata. BLAST analysis of the extracted 11,987 CDS revealed high sequence similarity with the termite Zootermopsis nevadensis; until recently, Z. nevadensis represented the most complete and well annotated Dictyopteran genome available at the time of analyses (Terrapon et al., 2014). The size of our transcriptome is comparable to recent transcriptomes for the

American cockroach, Periplaneta americana, containing 85,984 contigs with 17,744 annotated

(Kim et al., 2016), as well as for the lobster cockroach, Nauphoeta cinerea, which had 57,928 assembled contigs (Segatto, Diesel, Loreto, & da Rocha, 2018).

Differential expression analysis revealed 2,474 transcripts with significantly different expression relative to non-pregnant females enriched in predicted functions such as organonitrogen compound metabolism and protein metabolic processes. This heavy investment into nitrogen and protein metabolism is unsurprising considering the well documented maternally synthesized protein component of the milk-like secretion consumed by intrauterine developing embryos (Banerjee et al., 2016; Ingram et al., 1977; Marchal, Hult, Huang, Stay, &

38 Tobe, 2013; Stay & Coop, 1974; Williford et al., 2004). Through our male-female comparisons, we also identified a set of 286 genes with male specific expression. These genes generally lacked similarity to existing annotations in available databases but were enriched for processes such as metal ion binding, peptidase activity, proteolysis, and phosphatase activities among others. Commonly seen enriched in transcriptomes of male reproductive organs, these enzymatic activities have been linked to a variety of post-mating changes in female reproductive physiology such as ovulation and sperm storage (Bretãs et al., 2012; Gotoh et al.,

2018; McGraw, Gibson, Clark, & Wolfner, 2004; Sirot, Rubinstein, Avila, Wolfner, & LaFlamme,

2010). Male specific transcriptome libraries often contain sequences with little to no homology to known genes and functions (Attardo et al., 2019; Bretãs et al., 2012; Gotoh et al., 2018;

Meibers et al., 2019; Wei et al., 2016); this is attributed to what are known as male accessory gland or seminal proteins since they are undergoing rapid evolution (Attardo et al., 2019; Bretãs et al., 2012; Gotoh et al., 2018; Meibers et al., 2019; Sirot et al., 2010; Wei et al., 2016), explaining the lack of annotations associated with our male enriched gene set.

Our data also support the previously characterized increase in milk protein synthesis and secretion (Evans & Stay, 1994; Ingram et al., 1977; Stay & Coop, 1974; Williford et al.,

2004), confirming that this increase is controlled at the transcriptional level. It is exceptionally notable that while milk proteins are synthesized by the cells of the brood sac epithelium

(Hagan, 1941; Ingram et al., 1977; Stay & Coop, 1974; Williford et al., 2004), the magnitude of milk cDNA expression is so great that it is not masked by the sequencing of a whole-body RNA sample, despite the fact that this organ makes up a relatively small amount of the body mass of a female D. punctata. Juvenile hormone (JH), produced in the corpora allata (CA) of insects is

39 known for its roles in development and reproduction (de Kort & Granger, 1996; Engelmann &

Mala, 2000; Rankin & Stay, 1984; Rotin & Tobe, 1983; Stoltzman, Stocker, Borst, & Stay, 2000).

The fluctuations of this hormone have been extensively characterized across female reproduction in insects (de Kort & Granger, 1996; Rankin & Stay, 1984; Rotin & Tobe, 1983;

Stoltzman, Stocker, Borst, & Stay, 2000). During D. punctata reproduction, JH gradually increases in the first several days post mating, corresponding to oocyte development and vitellogenesis, peaking at roughly five days post mating at peak yolk deposition (de Kort &

Granger, 1996; Rotin & Tobe, 1983; Stoltzman et al., 2000; Tobe et al., 1985). Shortly after, JH levels drop indicative of vitellogenesis and ovipositioning into the brood sac, remaining low until the time of parturition (de Kort & Granger, 1996; Rotin & Tobe, 1983; Stoltzman et al.,

2000; Tobe et al., 1985). JH titers are modulated in part by juvenile hormone esterase (JHE) activity; the activity of this enzyme shows an inverse relationship with JH levels across reproduction indicating that combined with fluctuating biosynthesis (Marchal et al., 2013;

Paulson & Stay, 1987; Tobe et al., 1985), JHE maintains the low JH titers required for maintenance of pregnancy (de Kort & Granger, 1996; Rotin & Tobe, 1983; Stoltzman et al.,

2000; Tobe et al., 1985). Our RNA-seq analysis suggests that this increase in enzymatic activity is being controlled at the transcriptional level through increased expression of jhe, similar to what has been seen in the distantly related viviparous tsetse fly (Baumann et al., 2013). Of note, we did not see a change in the expression of transcripts encoding juvenile hormone biosynthesis corresponding to the JH titer as previously described in D. punctata (Couillaud &

Feyereisen, 1991; Hult et al., 2015; Marchal et al., 2013; Paulson & Stay, 1987; Stay, Tobe,

Mundall, & Rankin, 1983; Tobe et al., 1985) or other insects (Baumann et al., 2013; Borras-

40 Castells, Nieva, Maestro, Maestro, Belles and Martín, 2017; Dominguez & Maestro, 2017;

Ishikawa et al., 2012; Naghdi, Maestro, Belles, & Bandani, 2016; Qu, Bendena, Tobe, & Hui,

2018; Rankin, Palmer, Yagi, Scott, & Tobe, 1995). Although it is possible the synthesis of juvenile hormone is not downregulated during pregnancy, the production of juvenile hormone occurs in the corpora allata (Marchal et al., 2013; Paulson & Stay, 1987; Tobe et al., 1985), a part of the insect central nervous system that is relatively small compared to the amount of tissue from which RNA was extracted for this study. Consequently, it is probable that changes in expression of these transcripts are being masked by our sampling technique and tissue specific sampling would reveal differential expression of JH biosynthesis.

To further characterize the role of the differential expression of jhe transcripts, we performed an RNA interference knockdown of jhe. Our knockdown experiments suggest that this transcriptional regulation of jhe is a key component in maintaining milk production.

Interestingly, our knockdown was able to inhibit milk production within seven days of treatment but did not cause termination of any pregnancies in that time. Additionally, our knockdown produced unexpected patterns of expression of Khr1 and vitellogenin. Krh1 and Vg are known for their strong expression in response to JH (Baumann et al., 2013; Hult et al., 2015;

Konopova, Smykal, & Jindra, 2011; Marchal et al., 2013; Minakuchi, Namiki, & Shinoda, 2009;

Stay & Clark, 1971; Wilson, Landers, & Happ, 1983); however while our knockdown increased

JH enough to suppress milk protein production, it did not change the expression levels of Vg and in fact decreased expression of Krh1, which usually increases as JH levels rise. It is possible that the change in JH levels was high enough to prevent milk production but was not maintained at high enough level for sufficient time to trigger Vg transcription. As for Krh1, a

41 previous study increasing JH production by knocking down the retinoid X receptor/ultraspiracle

(RXR/USP) complex and ecdysone receptor (EcR) found organ-specific changes in expression of

Krh1 (Hult et al., 2015). Specifically, increased JH biosynthesis decreased Krh1 expression in corpora allata, increased it in the ovary, and expression in the fat body was not impacted by changes in JH alone (Hult et al., 2015). Additionally, we saw that during pregnancy, Krh1 levels are dramatically lowered after ovipositioning in D. punctata and are maintained low throughout pregnancy in our transcriptome. Consequently, we conclude that our knockdown did not produce or sufficiently sustain a significant enough increase in JH to trigger the expression of

Krh1 or Vg.

It is important to note, however, that viviparity and its associated physiology are not unique to D. punctata. Many other insects are live-bearing and even matrotrophic (Attardo et al., 2019; Clutton-Brock, 1991; Denlinger & Ma, 1974; Hagan, 1948; Ma, Denlinger, Järlfors, &

Smith, 1975; Roth & Willis, 1957; Tobe & Langley, 1978) including some dermapterans, aphids, and most notably tsetse flies (Glossinidae) and other Hippoboscoidea (Attardo et al., 2019;

Clutton-Brock, 1991; Denlinger & Ma, 1974; Hagan, 1948; Ma et al., 1975; Roth & Willis, 1957;

Tobe & Langley, 1978). Despite the phylogenetic distance separating Diploptera and Glossina, these genera have converged upon matrotrophic viviparity and employ similar regulatory mechanisms for the process (Attardo et al., 2019; Baumann et al., 2013; Denlinger & Ma, 1974;

Ejezie & Davey, 1976; Evans & Stay, 1989, 1995; Ingram et al., 1977; Langley & Pimley, 1986;

Marchal et al., 2013; Stay & Coop, 1974; Stay, Ostedgaard, Tobe, Strambi, & Spaziani, 1984;

Tobe et al., 1985; Tobe, Davey, & Huebner, 1973; Tobe & Langley, 1978; Williford et al., 2004).

Further underscoring this mechanistic convergence are the shared adverse reactions to

42 perturbations in juvenile hormone titers during pregnancy; JH inhibits production and secretion of milk proteins and can act as an abortifacient in both D. punctata and tsetse flies (Baumann et al., 2013; Denlinger, 1975; Evans & Stay, 1995; Langley & Pimley, 1986; Stay & Lin, 1981; Terr

Wee & Stay, 1987).

While viviparous reproduction is widespread throughout the animal kingdom, little is known about the molecular mechanisms underlying live-birth and pregnancy in insects. Despite numerous studies characterizing its reproductive endocrinology, little is known about other aspects of matrotrophic viviparity in Diploptera punctata, with most molecular research targeting hormonal processes occurring before fertilized eggs are ever deposited into the brood sac. The goal of this study was to expand our understanding of the molecular changes associated with this unique and complex reproductive process using RNA-seq analysis. In this study we report the first transcriptome analysis of D. punctata, focusing on characterizing changes in gene expression across pregnancy. We identified 11,987 putative protein coding sequences with 2,474 differentially regulated across pregnancy. Three hundred twenty-nine transcription factors were identified in this gene set, corresponding to 35 structural families, 10 of which are differentially regulated across pregnancy. The 25 previously sequenced milk protein cDNA clones were differentially expressed across all stages of pregnancy, being most highly expressed in the late lactation stages of pregnancy. In addition, we identified a significant pattern in the expression of juvenile hormone esterase (jhe), an enzyme previously shown to break down juvenile hormone in D. punctata that is associated with pregnancy maintenance. To better characterize the role of this differential expression, we knocked down jhe using RNAi, finding a significant reduction in milk protein transcript expression but no increase in

43 vitellogenin or krüppel homolog 1. In addition to providing new molecular resources for future studies into the biology of D. punctata, our study contributes to broadening the understanding of transcriptional changes associated with viviparity and enables further comparative research to be done among live bearing insects.

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55 Tables:

Table 1. Pregnancy stage determination. This table describes the measurements utilized to determine pregnancy stage based on a previous study by Stay & Coop (1973).

Reproductive Stage Embryo Length Estimated Embryo Age

Not Pregnant (NPF) Not present n/a

Pre Lactation (PreL) <1.6 mm 0-11 days

Early Lactation (EarL) 1.6-2.5 mm 12-27 days

Late Lactation (LateL) >2.5 mm 28-55+ days

56 Table 2. Primer and siRNA sequences utilized in this study

Gene Assay Sequence

elongation factor 1 a qPCR F: 5’—CAAGATTGGAGGTATTGGAACAGTG—3’

R: 5’—GACTTTACTTCAGTGGTCAAGTTGG—3’

vitellogenin qPCR F: 5’—AAAGGTGTCCTCAGCCAGC—3’

R: 5’—TCCTCCATCTCGGATTGGGA—3’

juvenile hormone esterase qPCR F: 5’—CCTGGACAAGGATGTTGTTATG—3’

R: 5’—CACCTCCGAAACTTGCTATG—3’

milk protein 13Y qPCR F: 5’—CAATATGGACAAGAGACACATCGTG—3’

R: 5’—CTGCAAGTATCCGACTTTCTGAATC—3’

krüppel homolog 1 qPCR F: 5’—ACACAGCGGCAAGTTACA—3’

R: 5’—AAGTTGACCGCTCTGGATAAA—3’

57 ecdysone receptor qPCR F: 5’—ATCAGTGAACGGAGTAAAACCTGTA—3’

R: 5’—TTGAGGTCATCATCAGAAGGTGATT—3’ juvenile hormone esterase siRNA F: 5’—rCrArArGrGrArUrGrUrUrGrUrUrArUrGrGrUrArArCrArATC—3’

R: 5’—rGrArUrUrGrUrUrArCrCrArUrArArCrArArCrArUrCrCrUrUrGrUrC—3’ green fluorescent protein siRNA F: 5’—rCrUrUrGrArCrUrUrCrArGrCrArCrGrUrGrUrCrUrUrGrUrArGrUrU—3’

R: 5’—rCrUrArCrArArGrArCrArCrGrUrGrCrUrGrArArGrUrCrArArG—3’

58 Table 3. General assembly information for both the full assembly and extracted CDS

All Contigs Extracted CDS

Number of contigs 102,880 11,987

Total size of contigs 56,694,784 10,820,347

Longest contig 24,290 23,307

Shortest contig 150 150

Mean contig size 551 903

Median contig size 314 693

N50 contig length 866 1326

%A 31.88 31.4

%C 18.22 19.74

%G 17.88 22.41

%T 32.01 26.45

59 Table 4. GO Term enrichment as generated by GSEA in CLC Genomics Workbench, t-statstics and P-values are from comparisons against non-pregnant females

Pre Lactation Early Lactation Late Lactation

Description GO ID Gene count T-Statistic P-value T-Statistic P-value T-Statistic P-value cuticle development 42335 26 -5.95 0.0087 -5.97 0.0018 -5.16 0.0009 transposition, DNA-mediated 6313 50 -1.99 0.4758 -6.97 0.0004 -5.54 0.0001 chitin-based cuticle development 40003 24 -6.17 0.0071 -6.69 0.0005 -5.90 0.0003 nucleic acid phosphodiester bond hydrolysis 90305 246 -14.60 <0.00001 -7.06 0.0009 -2.92 0.001 transposition 32196 50 -1.99 0.4758 -6.97 0.0004 -5.54 0.0001

DNA biosynthetic process 71897 187 -19.78 <0.00001 -10.49 <0.00001 -8.04 <0.00001

RNA-dependent DNA replication 6278 165 -19.41 <0.00001 -9.53 <0.00001 -8.25 <0.00001

DNA metabolic process 6259 415 -15.20 <0.00001 -8.86 <0.00001 -4.92 <0.00001 regulation of protein metabolic process 51246 185 -0.92 0.0506 2.41 0.0114 8.15 0.0004 translation 6412 401 6.84 <0.00001 13.00 <0.00001 11.67 <0.00001 single-organism cellular process 44763 1468 -5.89 0.0017 1.19 0.0002 11.77 0.0006 organic substance metabolic process 71704 3315 -13.01 0.0151 0.67 <0.00001 14.14 <0.00001

60 peptide biosynthetic process 43043 411 7.04 <0.00001 13.44 <0.00001 11.91 <0.00001 biosynthetic process 9058 1274 -5.95 0.0078 6.13 <0.00001 10.66 0.0009 regulation of cellular protein metabolic process 32268 179 -0.91 0.0532 2.39 0.0128 8.05 0.0006 organonitrogen compound metabolic process 1901564 944 2.56 <0.00001 7.72 <0.00001 11.26 <0.00001 organonitrogen compound biosynthetic process 1901566 601 5.79 <0.00001 11.73 <0.00001 12.10 <0.00001 cellular component assembly 22607 199 1.36 0.0008 5.56 <0.00001 8.75 0.0002 peptide metabolic process 6518 427 7.28 <0.00001 13.40 <0.00001 12.13 <0.00001 mRNA splicing, via spliceosome 398 59 2.61 0.0014 8.13 <0.00001 8.55 0.0006

RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 377 59 2.61 0.0014 8.13 <0.00001 8.55 0.0006 cellular metabolic process 44237 2935 -12.78 0.0721 0.33 <0.00001 13.62 0.0002 primary metabolic process 44238 3065 -13.50 0.1141 0.56 <0.00001 13.84 <0.00001

RNA splicing, via transesterification 375 59 2.61 0.0014 8.13 <0.00001 8.55 0.0006

61 reactions cellular biosynthetic process 44249 1210 -5.45 0.0056 6.43 <0.00001 10.59 0.0006 small molecule biosynthetic process 44283 125 1.09 0.0069 3.37 0.004 8.17 0.0004 cellular process 9987 3531 -15.72 0.3736 -1.41 <0.00001 13.44 0.0004 cellular amide metabolic process 43603 456 7.18 <0.00001 12.98 <0.00001 11.97 <0.00001 amide biosynthetic process 43604 420 7.51 <0.00001 13.76 <0.00001 12.21 <0.00001 macromolecule modification 43412 658 -12.45 0.0003 -4.57 0.146 2.84 0.259 organic substance transport 71702 452 -11.10 0.0008 -4.29 0.1324 0.56 0.0664 protein modification process 36211 602 -12.37 0.0001 -5.28 0.0602 2.16 0.1788 nucleobase-containing compound biosynthetic process 34654 486 -15.27 <0.00001 -5.48 0.0312 -1.63 0.0023 heterocycle biosynthetic process 18130 548 -14.20 <0.00001 -5.03 0.069 -0.31 0.0118 organic cyclic compound biosynthetic process 1901362 557 -14.15 <0.00001 -4.95 0.077 -0.14 0.0151 aromatic compound biosynthetic process 19438 525 -14.76 <0.00001 -5.31 0.0466 -1.00 0.0052

RNA phosphodiester bond hydrolysis, endonucleolytic 90502 46 -9.47 0.0003 -2.97 0.1006 -1.66 0.0651

62 nucleic acid metabolic process 90304 942 -15.18 <0.00001 -3.41 0.4912 2.91 0.1429 signal transduction 7165 539 -12.03 0.0003 -7.86 0.0007 1.17 0.0898 regulation of biological process 50789 1227 -14.48 0.0008 -6.93 0.0249 4.92 0.3858 cellular protein modification process 6464 602 -12.37 0.0001 -5.28 0.0602 2.16 0.1788 carboxylic acid metabolic process 19752 344 0.55 0.0001 2.84 0.0022 8.55 0.0015 single-organism metabolic process 44710 1534 -0.40 <0.00001 1.28 0.0001 11.00 0.0018 electron transport chain 22900 57 7.91 <0.00001 2.62 0.0238 5.80 0.0116 respiratory electron transport chain 22904 51 7.44 <0.00001 2.47 0.0296 5.23 0.0186 oxoacid metabolic process 43436 349 -0.03 0.001 2.40 0.0051 8.06 0.0027 mitotic cell cycle process 1903047 167 1.58 0.001 6.37 <0.00001 5.94 0.018 mitochondrial transport 6839 27 3.48 0.001 5.80 0.0004 5.56 0.0105 metabolic process 8152 4921 -15.84 0.0001 -4.73 0.0003 13.39 0.0022 inorganic ion transmembrane transport 98660 162 1.81 0.0005 3.27 0.0025 3.16 0.2502 inorganic cation transmembrane transport 98662 151 2.17 0.0004 3.74 0.0015 3.72 0.1555 mitochondrial electron transport, NADH to ubiquinone 6120 26 6.06 <0.00001 1.12 0.1634 2.89 0.1241 single-organism process 44699 2456 -6.68 <0.00001 -1.34 0.0011 11.28 0.0082

63 oxidation-reduction process 55114 813 3.62 <0.00001 1.78 0.0014 7.81 0.029 small molecule metabolic process 44281 600 0.81 <0.00001 2.92 0.0004 9.20 0.0016 generation of precursor metabolites and energy 6091 115 5.42 <0.00001 3.54 0.0028 7.05 0.0041 actomyosin structure organization 31032 24 3.99 0.0007 4.57 0.0027 4.98 0.0173 organic acid metabolic process 6082 350 0.02 0.0008 2.42 0.0048 8.08 0.0027 cell differentiation 30154 110 2.23 0.0005 7.71 <0.00001 7.46 0.0015 digestion 7586 10 5.13 0.0001 4.38 0.0056 5.05 0.0125 single-organism biosynthetic process 44711 318 -0.35 0.0033 3.33 0.0004 7.56 0.0063 nitrogen compound metabolic process 6807 1842 -8.68 0.0211 2.82 <0.00001 9.82 0.0237 synapse organization 50808 28 3.11 0.0022 5.38 0.0006 5.80 0.007 macromolecule biosynthetic process 9059 879 -6.93 0.2452 5.46 <0.00001 7.47 0.0513 neurogenesis 22008 89 0.99 0.0172 6.44 <0.00001 5.66 0.0154

RNA splicing 8380 74 1.74 0.0063 7.38 <0.00001 7.37 0.0023 cellular nitrogen compound metabolic process 34641 1620 -7.66 0.0131 4.92 <0.00001 10.45 0.0047 cellular macromolecule biosynthetic 34645 833 -6.51 0.2124 5.60 <0.00001 6.62 0.1186

64 process organic substance biosynthetic process 1901576 1220 -6.15 0.0149 6.10 <0.00001 10.33 0.0017 mRNA processing 6397 99 -0.02 0.0534 6.30 <0.00001 6.17 0.0086

RNA processing 6396 224 -3.33 0.332 4.76 0.0003 6.02 0.0277 cell cycle process 22402 204 0.68 0.0019 5.78 <0.00001 6.24 0.0165 ribonucleoprotein complex assembly 22618 49 0.94 0.0403 5.12 0.0005 5.23 0.0164 organelle organization 6996 311 -1.20 0.016 3.96 0.0003 6.52 0.0198 organelle assembly 70925 29 3.25 0.0016 5.43 0.0003 6.64 0.0047 ribonucleoprotein complex subunit organization 71826 50 0.80 0.0477 5.17 0.0005 5.27 0.0146 cellular component organization or biogenesis 71840 629 -4.13 0.0463 5.08 <0.00001 8.81 0.0022 macromolecule metabolic process 43170 2570 -13.89 0.4511 0.11 <0.00001 10.97 0.0193 cellular macromolecule metabolic process 44260 2015 -13.84 0.1242 0.69 <0.00001 9.33 0.0715 cellular protein metabolic process 44267 1009 -5.26 0.0182 4.11 <0.00001 9.15 0.0091 cellular developmental process 48869 229 -0.05 0.0069 6.38 <0.00001 7.61 0.0035 cellular nitrogen compound biosynthetic 44271 938 -6.16 0.0895 5.07 <0.00001 7.32 0.0707

65 process regulation of developmental process 50793 97 0.68 0.0217 4.95 0.0003 4.32 0.066 mRNA metabolic process 16071 115 -0.75 0.0971 6.11 <0.00001 6.72 0.0045 single-organism organelle organization 1902589 124 1.64 0.0016 4.19 0.0005 7.40 0.0018 cellular component organization 16043 596 -4.36 0.0783 4.34 <0.00001 7.99 0.0073 protein metabolic process 19538 1543 -7.30 0.0115 2.69 <0.00001 11.12 0.0016

66 Legends and Figures

Legends

Figure 1. (a) Female D. punctata giving birth (b) Micro CT scan of pregnant D. punctata

Figure 2. Quality metrics of the de novo assembly from Diploptera punctata (A) BUSCO representation in the complete combined assembly. (B) Top BLAST hits from the extracted CDS against the nr arthropod database

Figure 3. Functional annotation of genes differentially expressed across pregnancy. (A) GO terms enriched in the 2,474 transcripts differentially regulated across pregnancy. (B) Analysis of biological function GO term composition of significantly up and down regulated contigs in female D. punctata reveals unique transcriptional profiles for each stage of pregnancy.

Figure 4. Transcripts with male specific expression patterns (A) Venn diagram of genes with male specific expression relative to not pregnant and late lactation females as well as the core male gene set (B) GO term enrichment of the 284 male specific genes

Figure 5. Transcription factor identification and expression (A) 329 transcription factors belonging to 35 structural families were identified in our assembly. C2H2 zinc finger domains are the most abundant family, followed by homeodomain, bHLH, bZIP, and Myb domains (B)

TPM expression of ftz-f1 beta and Krh1 transcription factors in the four female stages surveyed.

* FDR p ≤ 0.01; ** FDR p ≤ 0.001

67 Figure 6. Differential expression of select reproduction associated genes in female D. punctata

(A) Milk protein mRNA clone expression (TPM) abundance across the four reproductive stages.

(B) Vitellogenin expression across pregnancy represented as TPM. (C) Expression of Juvenile hormone esterase across pregnancy represented as TPM. Significance is denoted by different letter combinations (FDR p-value < 0.05)

Figure 7. Relative mRNA expression of key transcripts associated with pregnancy after RNAi knockdown of JHE. * denotes a Wilcox test p-value <0.05

68 Figures

69 A B

Species Pediculus humanus corporis Zootermopsis nevadensisZootermopsis Stegodyphus mimosarum Coptotermes formosanus Coptotermes Metaseiulus occidentalis 0% Camponotus floridanus Culex quinquefasciatus Harpegnathos saltator Harpegnathos Acyrthosiphon pisum Acyrthosiphon Tribolium castaneum Megachile rotundata Blattella germanica Nasonia vitripennis Bombus impatiens Ixodes scapularis Fopius arisanus Diaphorina citri Diaphorina Daphnia pulex Aedes aegypti Bombyx mori 0 0.80% 0.81% 0.81% 0.82% 0.84% 0.86% 0.91% 0.93% 0.96% 1.01% 1.05% 1.12% 1.33% 1.47% 1.68% 1.79% 2.20% 2.25% 2.85% 25% 2000 B USCOs USCOs % 50% No. Top BLAST Hits 75% 4000 100% 57.30% 6000 Missing F CompleteSingle Co Duplicated Complete B r agmented USCO status p y

70 GO Name B A nucleobase-containing compound biosynthetic process

organonitrogen compound biosynthetic process cellular component organization or biogenesis organonitrogen compound metabolic process anatomical structure morphogenesis multicellular organism development anatomical structure development small molecule metabolic process carbohydrate derivative binding cellular amide metabolic process cellular component organization nucleic acid metabolic process peptide biosynthetic process amide biosynthetic process structural molecule activity peptide metabolic process protein metabolic process cytoskeleton organization developmental process organelle organization nucleic acid binding system development extracellular region transferase activity protein binding anion binding drug binding DNA binding translation cell cycle 0 1 2 3 4 5 6 Enriched Bar Chart 7 Female DEGs 8 9 10 11 12 13 % Seqs 14 15 16 17 18 19 20 21 22 23 24 25 26 27

71 GO Name oxidoreductase activity, or reduction of molecular oxygen acting on paired donors, with incorporation

B A

protein tyrosine/serine/threonine phosphatase activity protein tyrosine/serine/threonine peptidase activity, acting on L organonitrogen compound metabolic process catalytic activity, acting on a protein serine serine − type endopeptidase activity transition metal ion binding protein metabolic process serine hydrolaseserine activity − endopeptidase activity type peptidase activity − amino acid peptides peptidase activity hydrolase activity Pregnant catalytic activity proteolysis 1454 Male Male 546 Not vs vs 0

4 5 286 15 6 7

9 Lactation 10 11 Male Male Late 12 12 89 vs 13 14

16 Sequence count

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

72 A

TPM B

TPM 10000 20000 30000 40000 10000 20000 30000 40000 0 0 ftz − ftz f1 beta * − f1 beta

Transcription factor Transcription factor Krh1 ** Krh1

reorder(Group, order) LateL EarL PreL NPF reorder(Group, order) LateL EarL PreL NPF

73 A

A B B B C AB

1000 100 C A

reorder(Group, Order) reorder(Group, Order) 100 NPF NPF

PreL PreL EarL EarL

B LateL10 LateL

Vitellogenin expression (TPM) Vitellogenin expression 10

B Juvenile hormone esterase expression (TPM) hormone expression esterase Juvenile

1 1 NPF PreL EarL LateL NPF PreL EarL LateL Group Group

2.0

1.5

group siGFP siJHE 1.0 Relative mRNA expression Relative * 0.5

* * 0.0 JHE Krh1 MP13 Vg EcR Gene of interest

Chapter 3

Matrotrophic viviparity as a constraint on microbiome acquisition during

gestation in a live-bearing cockroach, Diploptera punctata

Emily C Jennings 1*

Matthew W Korthauer 1, Trinity L Hamilton 2, Joshua B Benoit 1

1 Department of Biological Sciences University of Cincinnati. Cincinnati, OH 45221

2 Plant and Microbial Biology and the BioTechnology Institute, College of Biological Sciences

University of Minnesota. St. Paul, MN 55108

* The work presented here was principally composed by Emily C Jennings with assistance from the listed co-authors.

76 Abstract

The vertical transmission of microbes from mother to offspring is critical to the survival, development, and health of animals. Invertebrate systems offer unique opportunities to conduct studies on microbiome-development-reproduction dynamics since reproductive modes ranging from oviparity to multiple types of viviparity are found in these animals. One such invertebrate is the live-bearing cockroach, Diploptera punctata. Females carry embryos in their brood sac, which acts as the functional equivalent of the uterus and placenta. In this study, 16S rRNA sequencing was used to characterize maternal and embryonic microbiomes as well as the development of the whole-body microbiome across nymphal development. We identified 50 phyla and 121 classes overall and found that mothers and their developing embryos had significantly different microbial communities. Of particular interest is the notable lack of diversity in the embryonic microbiome, which is comprised exclusively of Blattabacteriaceae, indicating microbial transmission of only this symbiont during gestation. Analysis of postnatal development reveals that significant amounts of non-Blattabacteria species are not able to colonize newborn D. punctata until melanization, after which the microbial community rapidly and dynamically diversifies. While the role of these microbes during development has not been characterized, Blattabacteria must serve a critical role providing specific micronutrients lacking in milk secretions to the embryos during gestation. Collectively, this research provides insight into the microbiome development, specifically with relation to viviparity, provisioning of milk- like secretions, and mother-offspring interactions during pregnancy.

77 Introduction

Animals share their bodies with a diverse suite of microorganisms known as the microbiome (Engel & Moran, 2013; The NIH HMP Working Group, 2009). These microbes have important roles in a variety of processes benefiting their host, ranging from nutrient metabolism to immunity {Formatting Citation}. For most animals, their microbial community is established over development through interactions with the environment, through diet, as well as interactions with other organisms (Abdul Rahman et al., 2015; Blaser & Dominguez-Bello,

2016; Carrasco et al., 2014; da Costa & Poulsen, 2018; Estes et al., 2013; Funkhouser &

Bordenstein, 2013; Gilbert, 2014; Korpela et al., 2018; Kostic et al., 2015; Morse et al., 2013;

Mueller, Bakacs, Combellick, Grigoryan, & Dominguez-Bello, 2015; Perez-Muñoz, Arrieta,

Ramer-Tait, & Walter, 2017; Schwab, Riggs, Newton, & Moczek, 2016; Shukla, Vogel, Heckel,

Vilcinskas, & Kaltenpoth, 2018; Torrazza & Neu, 2011; Wang & Rozen, 2017). Of interest is the role that parent-offspring interactions play in the microbial acquisition during early development, specifically from mother to her offspring (Adair & Douglas, 2017; Dimmitt et al.,

2010; Duranti et al., 2017; Fox & Eichelberger, 2015; Funkhouser & Bordenstein, 2013; Gilbert,

2014; Jašarević, Rodgers, et al., 2015; Korpela et al., 2018; Kostic et al., 2015; Perez-Muñoz et al., 2017; Schwab et al., 2016; Torrazza & Neu, 2011; Wade, 2014; Walker, Clemente, Peter, &

Loos, 2017).

The animal’s reproductive mode, in part, mediates the types of interactions mothers have with their offspring. Egg laying (oviparous) organisms have limited opportunity to pass microbes to offspring before they are born through hatching (Abdul Rahman et al., 2015; Bright

& Bulgheresi, 2010; da Costa & Poulsen, 2018; Estes et al., 2013; Funkhouser & Bordenstein,

78 2013; Salem, Florez, Gerardo, & Kaltenpoth, 2015; Schwab et al., 2016; Shukla et al., 2018). This forces vertical symbiont transmission to occur through incorporation during oogenesis or by inoculating the external egg surface for consumption immediately upon juvenile emergence

(Abdul Rahman et al., 2015; Estes et al., 2013; Funkhouser & Bordenstein, 2013; Schwab et al.,

2016; Shukla et al., 2018). Viviparous (live-bearing) animals can have extensive and complex interactions between mother and offspring during gestation and birth, the impacts of which can last for a few days to years (Cao-Lei et al., 2014, 2017; Duranti et al., 2017; Funkhouser &

Bordenstein, 2013; Jašarević, Rodgers, et al., 2015; Jiménez-Chillarón et al., 2015; Ma et al.,

2014; Ogawa & Miura, 2014; Poulin & Thomas, 2008; Stein & Lumey, 2000; Torrazza & Neu,

2011; Weiss et al., 2011). These prolonged interactions provide means for multiple routes of vertical transmission of microbes from mother to her progeny (Funkhouser & Bordenstein,

2013; Ma et al., 2014; Mueller et al., 2015). In humans, while placental transmission of microbes is debated (Aagaard et al., 2014; Blaser & Dominguez-Bello, 2016; Fardini, Chung,

Dumm, Joshi, & Han, 2010; Perez-Muñoz et al., 2017; Walker et al., 2017), mother to newborn transfer can occur during passage through the birth canal, breast feeding and throughout early postnatal development (Ballard & Morrow, 2013; Dahlen, Downe, Kennedy, & Foureur, 2014;

Duranti et al., 2017; Funkhouser & Bordenstein, 2013; Jašarević, Howerton, Howard, & Bale,

2015; Jašarević, Rodgers, et al., 2015; Korpela et al., 2018; Ma et al., 2014; Mueller et al., 2015).

Other live-bearing animals and their symbionts have evolved to utilize the extended gestation as a time to inoculate progeny with bacteria (Denlinger & Ma, 1975; Funkhouser & Bordenstein,

2013; Ma et al., 2014; Morse et al., 2013; Mueller et al., 2015; Wang et al., 2013). This is exemplified in tsetse flies and other members of Hippoboscoidea, where mothers utilize

79 nutritive secretions as a mechanism to transfer required symbiotic bacteria to their intrauterine developing larvae (Douglas, 2017; Morse et al., 2013; Snyder & Rio, 2015; Wang et al.,

2013; Weiss et al., 2011). For tsetse flies, symbiotic bacteria, specifically Wigglesworthia, provide key B vitamins that are low in their food source (blood) or within milk transferred to the developing intrauterine larva (Akman et al., 2002; Attardo et al., 2019; Rio et al., 2012).

Here, we examine shifts in the microbiome of the live bearing cockroach, Diploptera punctata, during pregnancy and development.

D. punctata reproduces by matrotrophic viviparity, in which embryos develop inside the brood sac, a unique organ which functions as both a uterus and pseudo-placenta, and are provided with nutrients by a secretion of milk-like components (Hagan, 1939, 1941, Roth &

Willis, 1955, 1957; Stay & Coop, 1973). This secretion appears in embryo gut contents at 20% of the 60-70-day pregnancy, when the dorsal edge of the body wall is closed (Ingram, Stay, & Cain,

1977; Roth & Willis, 1955; Stay & Coop, 1973, 1974). Diploptera milk is a combination of proteins and free amino acids, carbohydrates, and lipids in a water base (Ingram et al., 1977;

Stay & Coop, 1974; Williford, Stay, & Bhattacharya, 2004; Youngsteadt, Fan, Stay, & Schal,

2005). The proteins present include a unique family of lipocalin-like milk proteins (Ingram et al.,

1977; Stay & Coop, 1974; Williford et al., 2004). While this milky secretion provides vital nutrients to developing embryo, it is deficient in two essential amino acids, methionine and tryptophan (Ingram et al., 1977; Williford et al., 2004). It has been proposed that bacterial endosymbionts provide these two nutrients (Williford et al., 2004), however in oviparous cockroaches the only bacterium transmitted from mother to embryo belongs to the

Flavobacteria family Blattabacteriaceae (Bandi et al., 1994, 1995; Giorgi & Nordin, 1994). Most,

80 but not all, strains of Blattabacteria have an incomplete biosynthetic pathway for methionine

(Huang, Sabree, & Moran, 2012; Kambhampati, Alleman, & Park, 2013; López-Sánchez et al.,

2008, 2009; Patiño-Navarrete, Moya, Latorre, & Peretó, 2013; Sabree, Kambhampati, & Moran,

2009; Tokuda et al., 2013).

This study determined the microbiome of Diploptera punctata throughout development, characterizing the microbial communities inhabiting female D. punctata and their offspring across development using 16S rRNA gene sequencing. The information generated by this study will provide the first step in developing D. punctata as a model system to elucidate how intrauterine development and the prenatal microbiome affect later acquisition of microbial endosymbionts. Developing a new model system understanding microbial shifts during invertebrate matrotrophic viviparity will widen the evolutionary lens through which we view reproduction and the microbiome in viviparous animals.

Methods

Animals

Colonies reared at the University of Cincinnati (UC) Department of Biological Sciences

(Cincinnati, OH) were housed in a climate-controlled facility. Ambient temperature was held between 24-28°C and relative humidity (RH) was held between 70-80%. A 12:12 hour light-dark photoperiod was maintained for the duration of the experiment. Animals were provided water and fed Old Roy Complete Nutrition brand dog food (Mars, Inc.) ad libitum. A second group of

D. punctata were obtained from The Ohio State University (OSU) Biological Sciences

Greenhouse (Columbus, OH) insect collection where they were reared in similar conditions with

81 the exception of being fed a diet of Tetramin fish food (Spectrum Brands Pet). Upon retrieval, this sampling of the OSU colony was housed separately under identical conditions from the UC colony and provided the same food and water sources as the UC colonies.

Sample collection

Visibly pregnant females were selected from the colony for use in mother-embryo comparisons.

Females were surface sterilized by rinsing for 1 minute in each of the following solutions: 70% ethanol and 2% sodium hypochlorite. This was followed by four rinses in sterile phosphate- buffered saline (PBS; 81mM Na2HPO4, 19mM NaH2PO4, 150mM NaCl, pH 7.4). Embryo broods were then dissected from the brood sac in sterile PBS by making two small incisions at the opening of the brood sac, one on each side, and removed using ethanol sterilized forceps. To determine the developmental stage of the embryos, a single embryo from the center of each brood was measured on a bleach sterilized ruler and designated as pre-lactation, early lactation, or late lactation based on its length (Chapter 1, Table 1) (Stay & Coop, 1973). Entire broods of embryos and individual mothers were then placed into separate 1.5 mL centrifuge tubes with silica beads and stored at -80°C until processing. While mother-embryo pairs were collected for all three trimesters, only late lactation pairs were utilized in this study.

To characterize the postnatal development of the microbiome, visibly pregnant females were again selected from the colony and housed in individual containers with food and water ad libitum and monitored for active birthing. Nymphs were collected as neonates (identified by lack of cuticular melanization), first instars (identified by melanization within 12 hours of birth).

Second, third, and fourth instar nymphs were sampled and identified by size and the presence

82 of molts in the living quarters. Postnatal samples were collected only from the UC colonies.

Upon collection, nymphs were surface sterilized using the methods described above and then stored in 1.5 mL centrifuge tubes with silica beads at -80°C until processing.

Genomic DNA preparation

Samples were homogenized in 1µl of sterile 1X PBS and DNA was extracted using a QIAGEN

DNeasy Blood and Tissue Kit (QIAGEN). The homogenate (200µl) was incubated with proteinase

K (QIAGEN) over night before continuing the provided protocol. DNA concentration and quality were measured via A260/A280 and A260/A230 using a Nanodrop 2000 (Thermo Scientific). All samples were diluted to 20 ng/µl for sequencing.

16S rRNA Sequencing and Bioinformatic Analyses

The V4 hypervariable region of the bacterial 16S rRNA gene was PCR amplified using the 515f

(GTGYCAGCMGCCGCGGTAA) and 806r (GGACTACNVGGGTWTCTAAT) universal primers (Apprill,

Mcnally, Parsons, & Weber, 2015; Caporaso et al., 2011). Amplicon sequencing using the MiSeq

Illumina 2x300bp chemistry was conducted at the Miami University Center for Bioinformatics &

Functional Genomics (Oxford, OH, USA) as well as the University of Minnesota Genomics Center

(Minneapolis, MN, USA).

Using the Ohio Supercomputer Center resources (Ohio Supercomputer Center, 1987), sequence reads were processed in mothur (v.1.39.5) (Schloss et al., 2009) based on the published MiSeq SOP (Kozich, Westcott, Baxter, Highlander, & Schloss, 2013). Briefly, the make.contigs command was used to extract quality data from the reads and only reads

83 possessing a quality score greater than 25 were joined to make the contigs for further analysis.

Screen.seqs was utilized to remove contigs containing ambiguous bases, contigs longer than

275 bp, and those containing homopolymers longer than 8 bp. Unique.seqs and count.seqs were utilized to remove duplicate sequences and generate count tables. Taxonomic assignment of sequences was conducted using align.seqs to compare the contigs to the SILVA database

(v.123) (Quast et al., 2013) containing only the V4 region aligning with the primers used.

Filter.seqs was used to remove sequences that have large gaps in the alignments. Chimeric sequences were removed using the UCHIME (Edgar, Haas, Clemente, Quince, & Knight, 2011) algorithm using the chimers.uchime and remove.seqs commands. Non 16S rRNA gene sequences were removed using the classify.seqs and remove.lineage commands. Sequences were clustered using the cluster.split command at the taxonomic level 4, representing order. All further analyses were conducted using operational taxonomic unit (OTU) assignments generated in the above steps. Rarefaction curves were generated using the rarefaction.single and the number of observed OTUs (sobs), demonstrating adequate sequencing depth. Alpha diversity was assessed using the inverse Simpson, and Shannon diversity metrics. NMDS and

PCOA analyses were conducted using mothur. For mother-embryo comparisons, community composition was manually assessed for visualization at taxonomic level 5, representing bacterial families. In addition to mothur we performed a second analysis of our data for validation purposes utilizing QIIME (v. 1.9.1) (Caporaso et al., 2010) as implemented by the

Nephele pipeline (v. 2.2.2) (Weber et al., 2018) using the default settings, referencing the SILVA database (v.128 SSU REF 99) (Quast et al., 2013). When relative abundances calculated at the class level by both methods were compared, they were found to be significantly correlated

84 (Figure 1); consequently, results from mothur were reported. Additional results from the QIIME analysis can be found in supplemental data files 1 and 2.

Data processing was conducted in Microsoft Excel (v.16.22) and R (v.3.3.3) (R Core

Team, 2017) using RStudio (v1.1.423) (RStudio Team, 2015). Additional statistics and graphical representations of data were also performed in R using RStudio. Packages utilized include dplyr

(Wickham, Francois, Henry, & Müller, 2017), dunn.test (Dinno, 2017), ggplot2 (Wickham, 2016), reshape2 (Wickham, 2007), RColorBrewer (Neuwirth, 2014), Rmisc (Hope, 2013), and wesanderson (Ram & Wickham, 2018).

Results

Maternal and embryonic microbiomes

Amplicons from the 16S rRNA generated 2,180,632 paired end reads from both OSU and UC colony mothers and embryos of D. punctata, assembled into 2,170,187 contigs when joined. Of those, 1,759,259 total sequences passed quality control and were classified as Archaea (8,320 reads; 0.473%), Bacteria (1,750,772 reads; 99.518%), or unknown (167 reads; 0.009%). Removal of unwanted classifications (archeae, chloroplast, eukaryote, mitochondria, and unknown) yielded 1,749,921 merged reads, ultimately generating 38,969 bacterial operational taxonomic units (OTUs) corresponding to 44 phyla, 108 classes, 204 orders, 386 families, and 710 genera.

Overall, Bacteroidetes was the most prominent phylum (21,099 OTUs; 54.143%), followed by

Firmicutes (5,513 OTUs; 14.147%), Proteobacteria (4,783 OTUs; 12.274%), and unclassified bacteria (4,286 OTUs; 10.998%) (Figure 2). Flavobacteria, a class of Bacteroidetes, was the most prominent class overall (14,636 OTUs; 37.558%), with unclassified bacteria (4,286 OTUs;

85 10.998%) and Bacteroidia (4,042 OTUs; 10.372%) as the second and third most represented.

Orders followed suit with Flavobacteriales (14,636 OTUs; 37.558%), unclassified bacteria (4,286

OTUs; 10.998%) and Bacteroidales (3,897; 10.000%) as the only orders representing 10% or more of the total OTUs. At the family level, Blattabacteriaceae, a family of Flavobacteria, was the most represented overall in both OTUs (14,426 OTUs; 37.019%) and reads (1,038,785 reads;

59.047% of all reads including non-bacterial) with unclassified bacteria being the next most abundant family (4,286 OTUs; 10.998%) followed by unclassified Bacteroidetes (2,260; 5.799%) and Ruminococcaceae (1,890; 4.850%) (Figure 2).

In mothers, OTUs were distributed among the same top four phyla (Bacteroidetes,

35.354%; Firmicutes, 27.714%; Proteobacteria, 14.138%; unclassified bacteria, 11.609%), with a similar distribution among mothers of both the OSU and UC colonies. At the family level, OTUs derived from D. punctata mothers were most represented in Blattabacteriaceae (6,734;

13.842%), Ruminococcaceae (5,781; 11.883%) and unclassified bacteria (5,648; 11.609%)

(Figure 2). Mothers from the OSU and UC colonies had similar distributions of OTUs among families. Additionally, there was no significant difference between the two colonies in community diversity or evenness (Figure 3). We identified a core community of 2,314 OTUs shared between mothers of both colonies, composed of 25 phyla with Firmicutes and

Bacteroidetes representing more than 60% of OTUs (Figure 4). Clostridia and Clostridiales were the most abundant of the 47 classes and orders respectively, each making up just over 30% of the core community. No individual family represented more than 16% of the core OTUs, with

Ruminococcaceae (16%) being the most abundant of the top eight families (52%).

86 Approximately 89% of OTUs and 99% of sequencing reads from embryos of both colonies belonged to the family Blattabacteriaceae, while all other families individually represented 1% or less of OTUs and 0.08% of embryo derived sequencing reads (Figure 2).

These findings were corroborated by secondary analyses completed using the Nephele implementation of QIIME, despite inherent differences in computational methods (Figure 1).

Embryos of both UC and OSU colonies did not differ significantly in diversity, evenness, and species richness (Figure 3). However, microbial communities of embryos were less diverse and less so than mothers across both colonies (Figure 3). Analysis of molecular variance and homogeneity of molecular variance in mothur revealed that despite our four sampling groups consisting of mothers and embryos from two distinct colonies (Table 1 and Table 2), there exist three distinct sub communities corresponding to UC mothers, OSU mothers, and all embryos

(Figure 4).

While the transmission of the cockroach specific endosymbiont Blattabacteria is known to occur during oogenesis (Sacchi et al., 1996), surface sterilization of oothecae and hatching into a sterile environment results in a microbiome exclusively composed of Blattabacteria, indicating any other bacteria must be acquired from food or feces (Pietri, Tiffany, & Liang,

2018). Such is the case in the intergenerational transfer of microbiota via proctodeal trophallaxis in Cryptocercus punctulatus and Mastotermes darwiniensis (McMahan, 1969).

Because D. punctata harbor their developing embryos for their gestational period, it is possible other bacteria may be transmitted via the brood sac. The low diversity and overall OTUs present in embryonic samples, however, suggest that if other bacteria are transmitted during gestation the number is very low and is not likely of significance to D. punctata embryos. This

87 indicates that Blattabacteria are the main endosymbiont during intrauterine development in D. punctata and that any additional constituents of the microbiome colonize after birth.

Postnatal microbiome development

We next sought to determine the progression of the microbiome over postnatal development.

Because we found no significant differences between the OSU and UC colonies of D. punctata the samples were recategorized for subsequent analyses and denoted simply as mothers and embryos. To determine the succession of the microbial communities inhabiting D. punctata from embryo to adulthood, we surveyed the microbiome of neonate nymphs and each of the following nymphal instars (one through four).

A total of 6,453,793 paired reads from mothers, embryos, and juvenile instars were used to generate 6,443,348 contigs in mothur. Of these, 4,752,552 passed quality control and were able to be taxonomically classified as either Archeae (14,141; 0.298%), Bacteria

(4,737,007; 99.673%), or unknown (1,402; 0.029%). There were also two Eukaryotic reads

(<0.001%). Removing unwanted reads as before, 4,734,605 remained and were utilized to generate 209,554 bacterial operational taxonomic units (OTUs) including 50 phyla, 122 classes,

252 orders, 485 families, and 1008 genera. As expected, Bacteroidetes was again the most abundant phylum (122,945 OTUs; 58.670%) when all samples were combined, followed by

Firmicutes (29,705 OTUs; 14.175%), unclassified bacteria (24,777 OTUs; 11.824%), and

Proteobacteria (20,068 OTUs; 9.577%). Flavobacteria and unclassified bacterial classes comprised 54.979% of class-level OTUs, a trend that holds true at the order level as well. At the family level Blattabacteriaceae (42.877%) was again the most prominent taxon followed by

88 unclassified bacteria (11.824%), unclassified Bacteroidetes (5.515%), and Porphyromonadaceae

(4.506%) (Figure 5).

Bacteroidetes (94.985%), and more specifically Blattabacteriaceae (93.387%), again were the primary constituent of the embryonic microbial community, while other families each represented 0.716% or less of the OTUs present (Figure 5). The dominance of the microbial community by Blattabacteriaceae persisted after birth during the neonate stage (93.741%) with each other family representing less than 1% of the community (Figure 5). Post-melanization first instars, however, have a more diverse microbial community. While Bacteroidetes remains the major phylum represented, it represents a much smaller portion of the OTUs (53.563% down from the roughly 95% of embryos and neonates). Firmicutes and Proteobacteria are the next most abundant phyla. Blattabacteriaceae is still the most abundant family (40.442%), a significant portion of the community (a combined 23.494% of OTUs) is made up by unclassified bacteria (6.281%), Enterobacteriaceae (6.125%), unclassified Lactobacillales (5.737%), and

Porphyromonadaceae (5.351%), while all other families individually represented less than 4% of the first instar microbial community (Figure 5). Second instars had the same major phyla represented, but had more families represented in high levels. Blattabacteriaceae represented only 28.303% of the community, while unclassified bacteria (12.978%), Porphyromonadaceae

(7.728%), Ruminococcaceae (7.705%), and unclassified Bacteroidetes (5.854%) increased in representation and together make up 34.265% of the OTUs. This redistribution of abundance from Blattabacteriacieae is maintained after the second instar stage, with abundances in third and fourth instars remaining around 30% (Figure 5). Adult females had even lower abundances of Blattabacteriaceae, although it was still the most abundant family (18.826%). All families

89 represented less than 20% of the OTUs present, with Ruminococcaceae and unclassified bacteria being the only two with abundances greater than 10% (Figure 5).

Multiple measures of diversity varied across the life stages of D. punctata. While embryos and neonates did not differ in either the Shannon index or Inverse Simpson, all other instars and mothers were significantly different from embryos in both measures (Figure 6).

Neonates also did not differ from first instars but showed significant differences in both diversity metrics compared to second, third, and fourth instars as well as adult females. Second, third, and fourth instars, however, did not differ from each other or mothers in any diversity measure (Figure 6). While amova and homova analyses revealed slightly different relationships between the samples (Table 3 and Table 4), the analyses consistently showed that embryos and neonates differed from the other juvenile stages and adult females. These results further support our hypothesis that D. punctata acquire microbial endosymbionts (outside of

Blattabacteria), not through direct maternal transfer during gestation but in the days and weeks after birth, primarily during and after initial melanization during the first nymphal instar.

Discussion

We identified 50 phyla, 122 classes, 252 orders, 485 families, and 1008 genera as part of the overall Diploptera punctata microbial community. Our analyses revealed that Bacteroidetes,

Firmicutes, and Proteobacteria were the dominant phyla in addition to unclassified bacteria.

Previous studies have characterized microbial communities of cockroaches, primarily the gut microbiome. Consistent with our findings, Bacteroidetes, Firmicutes, Proteobacteria and unclassified bacteria are repeatedly found to be prominent members of adult cockroach

90 endosymbiont communities (Bauer et al., 2015; Bertino-Grimaldi et al., 2013; Carrasco et al.,

2014; Gontang et al., 2017; Kakumanu, Maritz, Carlton, & Schal, 2018; Pérez-Cobas et al., 2015;

Schauer, Thompson, & Brune, 2014; Tinker & Ottesen, 2016). Similar to our adult female samples, other studies have shown Porphyromonadaceae, Rikenellaceae, and Bacteroidaceae to be the most abundant families of Bacteroidetes; while Lachnospiraceae, Ruminococcaceae,

Clostridiaceae, and Lactobacillaceae are commonly represented Firmicutes (Bauer et al., 2015;

Bertino-Grimaldi et al., 2013; Carrasco et al., 2014; Gontang et al., 2017; Kakumanu et al., 2018;

Pérez-Cobas et al., 2015; Sabree & Moran, 2014; Schauer et al., 2014; Tinker & Ottesen, 2016).

Proteobacteria present in cockroach microbiomes often belong to the families

Desulfobacteraceae, Enterobacteriaceae, Desulfovibrionaceae (Bauer et al., 2015; Bertino-

Grimaldi et al., 2013; Carrasco et al., 2014; Gontang et al., 2017; Kakumanu et al., 2018; Pérez-

Cobas et al., 2015; Sabree & Moran, 2014; Schauer et al., 2014; Tinker & Ottesen, 2016). Most previous cockroach microbiome studies found extremely low representation of Blattabacteria or do not report on its abundance due to the specific sampling of gut tissue; Blattabacteria reside in the fat body and ovaries and thus will be lacking in studies focus on the gut microbiome (Bauer et al., 2015; Bertino-Grimaldi et al., 2013; Carrasco et al., 2014; Gontang et al., 2017; Kakumanu et al., 2018; Pérez-Cobas et al., 2015; Sabree & Moran, 2014; Schauer et al., 2014; Tinker & Ottesen, 2016). The few studies that performed microbiome analyses on whole bodies or carcasses without guts, however, report Blattabacteriaceae abundances ranging from 8% to 90% depending on the habitat sampled, although carcasses without guts were generally found to contain predominantly Blattabacteria (Carrasco et al., 2014; Kakumanu et al., 2018).

91 Investigations of developmental acquisition of the cockroach microbiome are rare, however one study characterized the succession of the microbiota in the oviparous German cockroach, Blattella germanica (Carrasco et al., 2014). Contents of surface sterilized oothecae contain exclusively Blattabacteria and whole bodies of first instar nymphs that hatched from unsterilized oothecae contain predominantly Blattabacteria, but have begun to acquire other gut symbionts (Carrasco et al., 2014). Despite the difference in reproductive mode, we found similar results in the intrauterine developing embryos and neonatal D. punctata.

One previous study has attempted to characterize the microbiome of D. punctata mothers and embryos, concluding that there are significant amounts of non-Blattabacteria microbes in embryos (Ayayee, Keeney, Sabree, & Muñoz-Garcia, 2017). In direct contrast, our embryo samples from two independent colonies, including the colony used in the previous study, produced sequencing reads that were 99.5% assigned to Blattabacteriaceae. Two taxa identified to be significantly enriched in the embryonic microbiome by this previous study were

Halomonadaceae and Shewanellaceae (Ayayee et al., 2017), neither of which were present in our maternal, embryo, or postnatal development samples. While our analysis using mothur did identify non-Blattabacteria sequences in embryonic samples, the extremely low abundances

(less than 0.5% of total raw reads combined) suggest they are sequencing artifacts or misidentified and are not likely critical for embryos during gestation. This is further supported by our secondary analysis using the Nephele implementation of QIIME, which identified no taxa representing more than 0.2% of the embryonic community other than Blattabacteria. Because of our robust sampling method, including two separately housed colonies of D. punctata from separate institutional origins and use of two independent pipelines for analysis, we conclude

92 that no bacterial transmission occurs after oogenesis during intrauterine development in D. punctata. Thus, Blattabacteria is the only bacterial component of the microbiome during intrauterine development. This is further supported by the lack of additional bacterial components in first instar nymphs collected immediately after birth (=neonate). While we cannot eliminate rearing differences, our study indicates that other bacteria, beyond

Blattabacteria, are not required for D. punctata development.

After determining that there was no significant gestational transmission of endosymbionts, we sought to characterize the microbial community across nymphal development. D. punctata juveniles have a minimum of three nymphal instars with females molting an additional time to a fourth instar stage. Newborn, unmelanized first instar nymphs did not differ in bacterial community from intrauterine developing embryos suggesting that significant bacterial transmission does not occur during the birthing process, unlike humans.

However, by the time first instars fully develop a hardened cuticle they have developed a more diverse microbial community where Blattabacteria represents only 35% of the OTUs. This substantial increase is likely the results of food and water consumption that occurs following melanization. Across the remaining instars, the community continues to become more diverse, however the changes become much less dramatic after the second instar stage. These findings are again consistent with a previous study investigating the juvenile microbiome of B. germanica as well as in other egg laying organisms such as burying beetle Nicrophorus vespilloides (Carrasco et al., 2014; Wang & Rozen, 2017). Consequently, we conclude that the microbial community is largely acquired during the first and second instar stages, likely from their environment where they cohabitate with both adult and other juvenile cockroaches, after

93 they have started to feed and drink There are continually changes to the microbiome throughout the life of the animal, but these are minor compared to the initial acquisition in early developmental stages.

This initial acquisition period of the microbiome is extremely important to animal development (Albenberg & Wu, 2014; Ballou et al., 2016; Breznak & Kane, 1990; Brownlie &

Johnson, 2009; Chung et al., 2012; Colston, 2017; Coon, Brown, & Strand, 2016; Coon, Vogel,

Brown, & Strand, 2014; Diaz Heijtz, 2016; Dimmitt et al., 2010; Hamdi et al., 2011; Kostic et al.,

2015; Lee & Brey, 2013; Ma et al., 2014; Malmuthuge, Griebel, & Guan, 2015; McFall-Ngai,

2014; Michalkova et al., 2014; Pais et al., 2008; Pietri et al., 2018; Schwab et al., 2016; Snyder &

Rio, 2015; Thompson, Rivera, Closek, & Medina, 2015; Torrazza & Neu, 2011; Wade, 2014; Yang et al., 2016). Studies in insect systems have demonstrated this by ablating the microbiome of juvenile animals and observing the phenotypes. Consistently, these experiments find that animals unable to acquire microbes from their environment or mothers face severe disadvantages, often failing to progress from one instar to the next, unable to molt to adulthood or undergo pupation, or dying. One example of this is the inability of axenic mosquito larvae to reach adulthood (Coon et al., 2016, 2014). In the dung beetle Onthophagus gazella, removal of a maternally provided fecal secretion, known as the pedestal, significantly reduces bacterial load in larvae hatched from surface sterilized eggs (Schwab et al., 2016).

While preventing microbiome acquisition in O. gazelle larvae doesn’t result in mortality as in mosquitoes, it is associated with reduced larval mass, increased time to adulthood, smaller adult body size, and impaired dehydration tolerance (Schwab et al., 2016). In tsetse flies,

Wigglesworthia glossinidia transmission via milk gland secretions is not only essential for B

94 vitamin provisioning, but also immune function (Weiss et al., 2011). Targeted elimination of this symbiont decreased the population of phagocytic hemocytes and reduced melanization ability

(Weiss et al., 2011). Symbiont community composition has also been implicated in insecticide resistance in the German cockroach (Pietri et al., 2018). Elimination of all bacteria from the cockroach except for Blattabacteria throughout development suggests that insecticide resistance are due to changes in non-Blattabacteria bacteria which are acquired after hatching

(Pietri et al., 2018). These studies underscore the importance of developing a diverse and robust microbial community during early nymphal development, which we have found primarily occurs during the first instar of D. punctata.

The embryonic microbiome comprised exclusively of Blattabacteria is of interest relative to the intra-uterine development of D. punctata embryos, as the milk-like secretion provided by mothers as the sole form of nutrition during development is largely devoid of two essential amino acids, methionine and tryptophan (Stay & Coop, 1974; Williford et al., 2004).

Consequently, it has been suggested that these amino acids are acquired from bacterial endosymbionts (Williford et al., 2004). Bacterial symbionts commonly serve to supplement nutrients that may be lacking in the diet (Bermingham & Wilkinson, 2009; Douglas, 2017; Engel

& Moran, 2013; Funkhouser & Bordenstein, 2013; Michalik, Szklarzewicz, Jankowska, &

Wieczorek, 2014; Michalkova et al., 2014). Viviparous insects, such as tsetse flies, take advantage of endosymbionts to fill such nutritional gaps during development, mostly through the provisioning of B vitamins (Douglas, 2017; Snyder, Mclain, & Rio, 2012; Snyder & Rio, 2015;

Wang et al., 2013). However, while Wolbachia is transmitted through the germ line before nutrient provisioning (Wang et al., 2013), other symbionts in these flies, such as Wigglesworthia

95 and Sodalis, have been shown to be transmitted from mother to offspring during their extended gestation period (Denlinger & Ma, 1975; Douglas, 2017; Snyder et al., 2012; Snyder &

Rio, 2015; Wang et al., 2013). The exclusively Blattabacterial composition of the microbiome in embryos suggests that this symbiont must be the source of these essential nutrients. However, previous studies characterizing the genome of Blattabacteria inhabiting other species of cockroaches have shown that only the strain belonging to the German cockroach (Blattella germanica) possesses the capability to synthesize methionine, one of the amino acids lacking in

D. punctata milk, in any capacity (Huang et al., 2012; Kambhampati et al., 2013; López-Sánchez et al., 2008, 2009; Neef et al., 2011; Patiño-Navarrete et al., 2013; Sabree et al., 2012, 2009;

Tokuda et al., 2013). Consequently, further investigation of this symbiotic relationship is required to understand the role of Blattabacteria during intrauterine development. Sequencing the genome of the D. punctata strain of Blattabacteria may reveal the presence of biosynthetic pathways that can provide amino acids required for prenatal development.

In conclusion, we provide a comprehensive survey of the microbial communities of mothers and their developing embryos along with succession of the microbiome community across postnatal development in D. punctata. This study provides evidence that, unlike other viviparous insects, there is no transmission of bacteria from mothers to offspring during their

63+ day pregnancy. Surprisingly, we also found no evidence that there is significant bacterial colonization of D. punctata during birth or within the few hours immediately following birth.

Rather, a majority of the microbiome components are acquired, likely from their environment, throughout the full duration of the first instar and melanization period. Further investigation will be required to further elucidate the specific mechanisms underlying nutrient provisioning

96 by Blattabacteria during embryonic development in D. punctata, as well as the role of the microbiome during nymphal development.

Data Accessibility

Sequence data have been added to the NCBI Sequence Read Archive (SRA) database

(PRJNA522760).

Author Contributions

E.C.J. and J.B.B. conceived the study. E.C.J. designed the experiments and, with guidance from

T.L.H., analyzed all data. E.C.J. and M.W.K. collected samples, performed DNA extractions and prepared samples for sequencing. T.L.H. coordinated sample transportation and sequencing.

E.C.J. and J.B.B. wrote the paper and T.L.H. edited the paper. E.C.J., M.W.K., T.L.H. and J.B.B. contributed substantially to interpreting the data and developing the manuscript and take full responsibility for the content of the paper.

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