Science, Faculty of
Zoology, Department of
DSpace
UBCV
Cyr, Elsa
Master of Science - MSc
University of British Columbia
Insects possess gustatory receptor neurons that respond to different taste modalities. These neurons express receptor proteins that are tuned to specific taste molecules to elicit a response in the neuron. Taste processing has been extensively studied in Drosophila melanogaster, which makes it an ideal model to identify molecular mechanisms conserved in other insects. In this study, we explore the neural basis of taste processing in the yellow fever mosquito, Aedes aegypti which utilizes its gustatory system to detect non-volatile chemosensory cues during behaviours such as blood feeding, nectar feeding, and egg laying. We hypothesize that taste processing mechanisms in A. aegypti are broadly conserved with D. melanogaster, but that mosquito-specific adaptations have evolved to support mosquito-specific behaviours. To test this, I produced a comprehensive anatomical and functional characterization of gustatory receptor neurons expressing taste receptors in the tarsi of mosquitoes. I used genetic driver lines to label sensory neurons, characterizing their anatomy and neural activity in response to taste stimuli. To investigate the anatomical map, I examined four different populations of neurons across all three legs in males and females. I found that specific populations of neurons are distributed throughout the legs, and across sexes in accordance with the behaviours they are associated with. To investigate the functional characterization of the neurons, I developed a novel protocol for live cell imaging in the tarsus during liquid tastant delivery. With this setup, I explored whether, similarly to D. melanogaster, neurons in the mosquito are broadly tuned to appetitive tastants, with overlapping populations of neurons responding to various appetitive taste molecules. I specifically tested sucrose, associated with mosquito nectar feeding behaviour, and lactic acid or low concentrations of sodium chloride, associated with blood feeding behaviour. My results show that non-overlapping populations of neurons in the mosquito respond to the appetitive tastants tested. These results are not consistent with observations in D. melanogaster but support the duality of the feeding systems in mosquitoes. Overall, this work aims to provide a better understanding of the neural coding of mosquito gustation for better understanding of the molecular basis of various key behaviour in mosquitoes.
The Neural Basis of Taste Coding in Aedes aegypti Mosquitoes by Elsa Cyr BSc, The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2024 © Elsa Cyr, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The Neural Basis of Taste Coding in Aedes aegypti Mosquitoes submitted by Elsa Cyr in partial fulfilment of the requirements for the degree of Master of Science in Zoology Examining Committee: Dr. Benjamin Matthews, Assistant Professor, Zoology, UBC Co-supervisor Dr. Michael Gordon, Professor, Zoology, UBC Co-supervisor Dr. Kota Mizumoto, Associate Professor, Zoology, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Elizabeth Rideout, Associate Professor, Cell and Development Biology, UBC Supervisory Committee Member iii Abstract Insects possess gustatory receptor neurons that respond to different taste modalities. These neurons express receptor proteins that are tuned to specific taste molecules to elicit a response in the neuron. Taste processing has been extensively studied in Drosophila melanogaster, which makes it an ideal model to identify molecular mechanisms conserved in other insects. In this study, we explore the neural basis of taste processing in the yellow fever mosquito, Aedes aegypti which utilizes its gustatory system to detect non-volatile chemosensory cues during behaviours such as blood feeding, nectar feeding, and egg laying. We hypothesize that taste processing mechanisms in A. aegypti are broadly conserved with D. melanogaster, but that mosquito-specific adaptations have evolved to support mosquito-specific behaviours. To test this, I produced a comprehensive anatomical and functional characterization of gustatory receptor neurons expressing taste receptors in the tarsi of mosquitoes. I used genetic driver lines to label sensory neurons, characterizing their anatomy and neural activity in response to taste stimuli. To investigate the anatomical map, I examined four different populations of neurons across all three legs in males and females. I found that specific populations of neurons are distributed throughout the legs, and across sexes in accordance with the behaviours they are associated with. To investigate the functional characterization of the neurons, I developed a novel protocol for live cell imaging in the tarsus during liquid tastant delivery. With this setup, I explored whether, similarly to D. melanogaster, neurons in the mosquito are broadly tuned to appetitive tastants, with overlapping populations of neurons responding to various appetitive taste molecules. I specifically tested sucrose, associated with mosquito nectar feeding behaviour, and lactic acid or low concentrations of sodium chloride, associated with blood feeding behaviour. My results iv show that non-overlapping populations of neurons in the mosquito respond to the appetitive tastants tested. These results are not consistent with observations in D. melanogaster but support the duality of the feeding systems in mosquitoes. Overall, this work aims to provide a better understanding of the neural coding of mosquito gustation for better understanding of the molecular basis of various key behaviour in mosquitoes. v Lay Summary As humans, we predominantly associate the taste system with eating. It indicates whether something is delicious and nutritive, or whether it is spoiled or toxic. Interestingly, certain animals, such as insects, use their taste systems to inform multiple other behaviours. In mosquitoes for instance, they may use this sensory system to detect favourable or repulsive cues when they are feeding, but also when searching for a place to lay their eggs, or even when mating. In this thesis, I characterize some of the sensory cells involved in these processes. I also aim to understand how they encode responses to various cues, and whether this is conserved in other insect species. My results indicate that unlike the fruit fly, mosquitoes encode certain appetitive cues through different cells. vi Preface All experiments were performed by Elsa Cyr in the Matthews lab at the Biological Sciences Building at UBC, the Gordon lab at the Life Science Institute at UBC, and the Neuroimaging and Neurocomputation Centre (NINC) located at the Djavad Mowafaghian Centre for Brain Health (DMCBH) in the UBC Hospital. The experiments were designed and supervised by Dr. Benjamin Matthews and Dr. Michael Gordon. Experimental questions and project directions were made in collaboration between Dr. Benjamin Matthews, Dr. Michael Gordon, and Elsa Cyr. Data analysis, figure generation, pictures (unless otherwise indicated), and illustrations were made by Elsa Cyr using Microsoft Excel, Adobe Illustrator, and GraphPad Prism. Cell counts for the anatomy section were performed by Elsa Cyr, with help from Hanna Elford and Layla Ali, under the direct supervision of Elsa Cyr. All yellow mutant mosquito strains used in this thesis were generated by members of the Conor McMeniman lab at John Hopkins University. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Chapter 1: Introduction ............................................................................................................... 1 1.1 Project overview ......................................................................................................... 1 1.2 Aedes aegypti gustation............................................................................................... 5 1.2.1 Anatomy of gustatory system ............................................................................. 5 1.2.2 Taste receptor families in insects ........................................................................ 7 1.2.2.1 Ionotropic receptors ........................................................................................ 8 1.2.2.2 Gustatory receptors ......................................................................................... 9 1.2.2.3 Transient receptor potentials ......................................................................... 10 1.2.2.4 Pickpocket receptors ......................................................................................11 1.2.3 Taste coding principles in insects ..................................................................... 11 1.3 Sexual dimorphism in mosquitoes ............................................................................ 12 1.4 Appetitive taste in mosquitoes .................................................................................. 13 1.4.1 Nectar and blood meals in mosquitoes ............................................................. 13 1.4.2 Sucrose, lactic acid, and sodium chloride ......................................................... 15 1.5 Taste responses in Aedes aegypti mosquitoes ........................................................... 16 viii 1.5.1 Sensilla electrophysiology recordings in the mosquito .................................... 16 1.5.2 Calcium imaging of sensory neurons ................................................................ 17 1.5.3 Molecular techniques to study the gustatory system ........................................ 18 1.6 Insect neuroanatomy in the tarsi ............................................................................... 19 1.7 Technical details ....................................................................................................... 20 1.7.1 Q-system driver-reporter construct ................................................................... 20 1.7.2 GCaMP ............................................................................................................. 22 1.8 Experimental objectives ............................................................................................ 22 1.8.1 Anatomy of the mosquito taste system ............................................................. 23 1.8.2 Functional profiling of the mosquito taste system ............................................ 23 Chapter 2: Methods .................................................................................................................... 25 2.1 Aedes aegypti mosquito strains ................................................................................. 25 2.2 Mosquito rearing and maintenance ........................................................................... 25 2.3 Anatomical data methods .......................................................................................... 28 2.3.1 Mosquito strains ................................................................................................ 28 2.3.2 Sample preparation ........................................................................................... 28 2.3.3 Confocal imaging .............................................................................................. 29 2.4 Functional data methods ........................................................................................... 30 2.4.1 Mosquito strains ................................................................................................ 30 2.4.2 Inverted widefield fluorescence imaging .......................................................... 30 2.4.3 Sample Preparation ........................................................................................... 30 2.4.4 Sucrose cell experiment protocol ...................................................................... 31 2.4.5 Sucrose cell experiment inclusion requirements............................................... 32 ix 2.5 Data analysis ............................................................................................................. 34 2.6 Statistical analysis ..................................................................................................... 34 2.7 Data availability ........................................................................................................ 34 Chapter 3: Results....................................................................................................................... 35 3.1 Anatomy study .......................................................................................................... 35 3.1.1 Driver comparisons ........................................................................................... 35 3.1.2 Sex and leg comparisons ................................................................................... 39 3.1.3 Segment comparisons ....................................................................................... 41 3.2 Functional profiling of the taste system in mosquito tarsi ........................................ 51 3.2.1 Proof that setup works ...................................................................................... 51 3.2.2 Sucrose cell experiment .................................................................................... 54 Chapter 4: Discussion ................................................................................................................. 60 4.1 Characterization of the anatomy of GRN populations in the tarsus of Aedes aegypti mosquitoes ............................................................................................................................ 60 4.2 Functional imaging protocol ..................................................................................... 63 4.2.1 Troubleshooting the functional imaging protocol............................................. 64 4.2.2 Non-overlapping cells respond to attractive tastants in the mosquito tarsus .... 65 4.3 Future directions ....................................................................................................... 66 4.3.1 For anatomy ...................................................................................................... 66 4.3.2 For functional imaging setup ............................................................................ 68 Bibliography .................................................................................................................................70 x List of Tables Table 2.1. Aedes aegypti genetic strains ..................................................................................... 25 xi List of Figures Figure 1.1 – Mosquito behaviours driven by the gustatory system .......................................... 4 Figure 1.2 – Illustration of Aedes aegypti trichoid sensillum on tarsus .................................... 7 Figure 1.3 – Schematic of Q-system .......................................................................................... 21 Figure 2.1 – Fluorescence in mosquito larvae .......................................................................... 27 Figure 2.2 – Anatomical imaging sample preparation............................................................. 29 Figure 2.3 – Functional imaging setup ...................................................................................... 31 Figure 3.1 – Confocal images of fifth tarsomere ...................................................................... 36 Figure 3.2 – Whole tarsi cell count comparison between different driver lines .................... 38 Figure 3.3 – Whole tarsi cell count comparison of legs and sexes .......................................... 40 Figure 3.4 – Pan neuronal total cell count ................................................................................ 42 Figure 3.5 – Ir25a total cell counts............................................................................................. 43 Figure 3.6 – Gr4 total cell counts ............................................................................................... 44 Figure 3.7 – ppk301 total cell counts .......................................................................................... 45 Figure 3.8 – Pan neuronal segment densities ............................................................................ 47 Figure 3.9 – Ir25a-expressing cells segment densities .............................................................. 48 Figure 3.10 – Gr4-expressing cells segment densities............................................................... 49 Figure 3.11 – ppk301-expressing cells segment densities ......................................................... 50 Figure 3.12 – Proof of principle responses in functional setup ............................................... 53 Figure 3.13 – Trace responses to tastants .................................................................................. 56 Figure 3.14 – Individual trace responses to tastants ................................................................ 57 Figure 3.15 – Separate cell populations respond to appetitive tastants ................................. 59 xii List of Abbreviations ANOVA: analysis of variance AU: airy units CO2: carbon dioxide DEG/ENaC: degenerin/epithelial sodium channel ESID: electronically switchable illumination and detection module GFP: green fluorescent protein GR: gustatory receptors GRN: gustatory receptor neuron IR: ionotropic receptors mM: millimolar NaCl: sodium chloride PPK: pickpocket ROI: region of interest SEM: standard error of the mean SEZ: subesophageal zone TRP: transient receptor potential xiii Acknowledgements First and foremost, I want to thank my co-supervisors Dr. Ben Matthews and Dr. Mike Gordon. Thank you for giving me the opportunity to work in your labs, for your support throughout the project, your encouragements to pursue new things, and all your words of wisdom. I always felt that I could rely on you for support when I needed help, and you were very skilled at steering me in the right direction when I might be going off track. Your guidance in how to approach problems, how to implement solutions, and how to communicate it all has been invaluable. I truly believe that you have made me a better scientist. You are both incredible, attentive, and supportive mentors. Thank you for everything. I would also like to thank Dr. Liz Rideout, my supervisory committee member. Your inputs into my project have been very valuable, and always help me take a step back and appreciate the things I might not have considered. I also want to thank my mentees, Hanna Elford and Layla Ali. You both helped me with counting cells from stack images, which saved me so much time. Thank you to Jeff LeDue, the managing director of the NINC facility, who helped immensely when I was getting started there. Thank you also to members of the Conor McMeniman lab at John Hopkins University, who let us use their unpublished strains of yellow mutant mosquitoes. Thank you to all past and present members of both the Matthews and Gordon labs. A large thank you to Nick Tochor for all your help with my experimental setups, and all your wonderful, out-of-the-box ideas. Thank you to Leisl Brewster and Jonathan Chiang, for your suggestions and support, and always running through ideas with me. Thank you to Sasha McDowell and Jinfang Li for your suggestions and attentive help with programs. Finally, I want to thank all of my friends, in and out of UBC, as well as my family. You have always given me the moral support I needed. Thank you to Pasha for always listening and being there, and also for all the computer help. Thank you to Adèle, for forcing me to take well-needed breaks. Thank you to my mom, who has always supported me and probably read everything I’ve ever written. Thank you to my dad for always being there for me. 1 Chapter 1: Introduction 1.1 Project overview Mosquitoes are considered the deadliest animal due to their disease transmission, which occurs while they are blood-feeding on their host. There are 3586 described species of mosquitoes, 894 of which can transmit pathogens (Yee et al., 2022). The majority of mosquito disease vectors belong to the Aedes, Anopheles, or Culex genera (Yee et al., 2022). Pathogens that are transmitted by mosquitoes include arboviruses, bacteria, Plasmodium spp., and nematodes (Yee et al., 2022). This thesis will focus on the anthropophilic (preference for humans) mosquito species Aedes aegypti, commonly referred to as the yellow fever mosquito. It is a vector for arboviruses that can causes diseases such as yellow fever (Flaviviridae), dengue (Flaviviridae), zika (Flaviviridae), and chikungunya (Togoviridae) (Wint et al., 2022). The life cycle of Ae. aegypti consists of four stages. The adult female mosquito must blood feed to acquire the appropriate nutrients to develop eggs (Christophers, 1960). The gravid adult female mosquito will lay eggs above a waterline where they can survive for up to 8 months. Once the eggs are submerged in water, they will hatch into larvae, which are aquatic. The larvae go through 4 instars, usually around 5 days, until they become pupae, still in water. They will remain in the pupal stage for 2-3 days until they emerge as adults, when they can start flying (Christophers, 1960). They can live for a few weeks as adults, during which time the females mate with males, feed on blood, and lay their eggs (Day, 2016). Mosquitoes, like other insects, use many different sensory systems to interact and respond to the world around them. They will detect and process different types of cues from varying distances to drive appropriate behaviours. The broad sensory systems described in 2 insects are vision, the auditory system, somatosensation, and chemosensation, which includes olfaction and gustation (Montell, 2021). Although these are separate systems, they share some characteristics, and sometimes work together to drive certain behaviours. For instance, mosquitoes will use their visual system to detect dark contrast, their thermosensory system to detect heat, and their olfactory system to detect odours and carbon dioxide, all of which will help guide them from a long range to a host for blood feeding (Liu & Vosshall, 2019; Zwiebel & Takken, 2004). They can also use their auditory system to detect human noises for host-seeking or flight tones of other mosquitoes for mating purposes (Menda et al., 2019). Finally, at a closer range, they can use their gustatory system to guide their feeding, mating, and egg-laying behaviours (Baik & Carlson, 2020). Although they may use other senses to recognize potential mates, once closer, they can use non-volatile pheromones to initiate mating attempts (Baik & Carlson, 2020). Female mosquitoes will, in part, then use their gustatory system to determine whether a site is suitable for egg-laying (Afify & Galizia, 2015; Matthews et al., 2019). The mosquito uses its chemosensory system to interact with the chemicals in their environment. Their olfactory system is used to detect volatile chemicals and their gustatory system is used to detect non-volatile chemicals (Baik & Carlson, 2020). These chemosensory systems are vital in responding to chemicals in the environment and carrying out important behaviours. For instance, a host will secrete volatile chemicals that the mosquito will detect using their olfactory system to initiate host-seeking behaviour. The host will also secrete non-volatile chemicals, that the mosquito will detect using their gustatory system to initiate blood-feeding behaviour. Here, we attempt to understand the underlying molecular and cellular basis of the coding principles in the mosquito gustatory sensory system. Mosquito behaviours are key for the species 3 to thrive and in turn effectively spread disease. Many of these behaviours are in part driven by the gustatory system. These mostly include feeding, mating, and oviposition (or egg-laying) behaviours (Baik & Carlson, 2020) (Figure 1.1). Mosquitoes feed on sugars and blood for different functions. Adult mosquitoes of both sexes can sustain themselves on a sugar diet, usually from plant nectar, honey dew, or plant sap. Male mosquitoes rely solely on sugar meals for nutrients. Female mosquitoes of certain species only need a blood meal to obtain the nutrients necessary for egg production (Christophers, 1960). This meal is obtained through their biting behaviour, and in the case of an anthropophilic mosquito such as Ae. aegypti, that is predominantly from human hosts. 4 Figure 1.1 – Mosquito behaviours driven by the gustatory system (A) Ae. aegypti female blood-feeding (photo credits: Benjamin Matthews), (B) Ae. female mosquito nectar-feeding (photo credits: Marie Delport), (C) Ae. aegypti female mosquito on water surface (photo credits: Alex Wild), (D) One Ae. aegypti female and two male mosquitoes attempting to mate (photo credits: Alex Wild). 5 1.2 Aedes aegypti gustation Mosquitoes, like other insects, utilise their gustatory system to inform behavioural responses. Their taste system is, in large part, involved in making decisions for feeding. They are able to detect stimuli from the five main human taste modalities, which include sugars (sweet), salt, bitter compounds, acids (sour), and amino acids (umami) (Kaushik & Kain, 2020). This can inform the mosquito of the environment around them and elicit an appropriate response. For instance, sugars, low concentrations of sodium (< 250 mM), and amino acids can indicate that food is nutritive and tend to elicit positive feeding responses. Bitter, high concentrations of salt, and acids on the other hand tend to indicate that something is toxic and elicit negative responses that inhibit feeding (Baik & Carlson, 2020). The general valence of these taste modalities is conserved across insect species, including in the model organism Drosophila melanogaster (Kaushik & Kain, 2020). Although mosquito lineages and D. melanogaster, commonly referred to as the fruit fly, are separated by 260 million years of evolution (Behura et al., 2011), there are many aspects of their gustatory systems that are conserved. We can therefore use D. melanogaster as a model for predicting principles of mosquito taste. 1.2.1 Anatomy of gustatory system Like other insects, such as D. melanogaster, the mosquito’s gustatory organs are distributed throughout their body. Taste organs in the mosquito include the tarsi of the legs, the labellum, the labrum, and the cibarium (Baik & Carlson, 2020; Owen, 1963). In this thesis, we will focus on the tarsus, which is the first organ that will make contact with a substrate when the 6 mosquito lands on it. The tarsus consists of five distinct segments called tarsomeres, numbered from the most proximal (1) to the most distal (5) (Super, 1962). Taste cells, or gustatory receptor neurons (GRNs), in the gustatory organs will detect taste molecules on a substrate and send a signal to the central nervous system for decision-making. The cell bodies of GRNs are in the taste organ and their dendrites extend into hair-like structures called sensilla that protrude from the organs. At the tip each sensillum there is a pore through which the dendrites make physical contact with taste molecules (Singh, 1997). The general anatomy of the mosquito’s taste system bears many similarities with that of the fruit fly (Baik & Carlson, 2020; Kaushik & Kain, 2020). On the tarsus of mosquitoes, the primary type of sensory hair is the trichoid sensillum, which will extend outward and have a single pore at the tip (Figure 1.2). There are typically anywhere from one to twelve sensilla on each segment of the three legs in females and one to eight in males (Morita et al., 2023; Super, 1962). Trichoid sensilla are usually innervated by up to five GRNs and one mechanosensory neuron (McIver & Siemicki, 1978). Each GRN expresses taste receptors that are tuned to specific taste molecules, which will elicit a response in the neuron. Neurons in the periphery that are involved in taste detection then send a signal to the central nervous system, either to the subesophageal zone (SEZ) of the brain or the ventral nerve cord, depending on the organ the signal is coming from (Matthews et al., 2019). Inputs from the legs specifically, project to the ventral nerve cord, innervating different neuromeres (Matthews et al., 2019). 7 Figure 1.2 – Illustration of Aedes aegypti trichoid sensillum on tarsus Schematic of a full leg of mosquito, labelled with each segment. Tarsus is in blue and each tarsomeres is numbered. The trichoid sensillum on the tarsus of mosquito typically contains five gustatory receptor neurons and one mechanosensory neuron. Cell bodies are in the tarsus, and their dendrites extend into the sensilla. There is a pore at the tip of the sensillum, through which dendrites make physical contact with non-volatile taste molecules. 1.2.2 Taste receptor families in insects Responses in GRNs are thought to follow from the receptors that they express. Several large multi-gene families were identified to be involved in taste perception in insects. These include ionotropic receptors (IRs), gustatory receptors (GRs), transient receptor potential (TRPs), and pickpocket receptors (PPKs) (Kaushik & Kain, 2020). Many members of these receptor gene 8 families have been identified in the detection of various taste modalities. Most of the knowledge on these gene families comes from D. melanogaster (Freeman & Dahanukar, 2015; Montell, 2021; Ni, 2021), although more recent studies have drawn conclusions from the D. melanogaster model to find orthologs in mosquitoes and other species, and compare their role in species-specific behaviours (Croset et al., 2010; Latorre-Estivalis et al., 2021). One striking example of this is for the receptors involved in the detection of carbon dioxide (CO2). In D. melanogaster, two gustatory receptors, Gr21a and Gr63a, are co-expressed and work together in the detection and response to CO2 (Jones et al., 2007; Kwon et al., 2007). Orthologs of these genes were identified in multiple insect species, including Ae. aegypti. AaegGr1 and AaegGr3 are the orthologs of DmelGr21a and DmelGr63a respectively (Robertson & Kent, 2009). In Ae. aegypti, Gr1, Gr2, and Gr3 make up subunits of the heteromeric CO2 receptor (Kumar et al., 2020; McMeniman et al., 2014; Robertson & Kent, 2009). 1.2.2.1 Ionotropic receptors Ionotropic receptors (IRs) are a very diverse subfamily of variant ionotropic glutamate receptors, which form functional ion channels (Benton et al., 2009). They are important in sensory systems in insects, including the sensation of taste, smell, temperature, and humidity, and tend to be localized in the periphery (Montell, 2021; Ni, 2021). This family of receptors was first identified in D. melanogaster, and were typically described for their role in olfaction, but have also been implicated in gustation as well (Benton et al., 2009; Croset et al., 2010). Several orthologs of D. melanogaster IRs have been identified in Ae. aegypti (Croset et al., 2010; Matthews et al., 2018). The ionotropic receptor gene family consists of 66 genes in D. melanogaster (Montell, 2021). In Ae. aegypti, 135 ionotropic receptor genes have been identified 9 (Matthews et al., 2018). Ionotropic receptors have a two-part extracellular ligand-binding domain, three transmembrane domains, and a pore loop (Montell, 2021). Typically, an IR functional channel will comprise of multiple IR subunits, one that is tuned to a specific stimulus and one or two broadly expressed co-receptors (Ni, 2021). IRs are found on all gustatory organs of D. melanogaster and are co-expressed with gustatory receptors in GRNs. Ir25a and Ir76b are broadly expressed co-receptors that can be expressed together or alone to make IR channels functional and mediate a response to a tastant (Ni, 2021). In this thesis, we will focus on Ir25a, which is often expressed along with other sensory receptors to make a functional channel. Ir25a contains the most sequence similarity to outgroups, making it the most basal IR, and it was found to be conserved across many insect species (Croset et al., 2010; Ni, 2021). It is highly important in chemosensation and was found to contribute to the detection of a variety of odorant and gustatory molecules. It is expressed in all gustatory organs and contributes to the response of different taste modalities (Ni, 2021). 1.2.2.2 Gustatory receptors The gustatory receptor (GR) gene family was first identified in D. melanogaster (Clyne et al., 2000). The GR gene family consists of 68 genes in D. melanogaster, that have quite diverse sequences, and sensitivities (Kaushik & Kain, 2020). In Ae. aegypti, 72 gustatory receptor genes have been identified (Matthews et al., 2018). Gustatory receptors serve a function in the sensation of taste, smell, light, and temperature (Montell, 2021). Gustatory receptors are perhaps the most ancient family of chemoreceptors in insects, but they have no homologs in mammals (Montell, 2021). They are ion channels that contain seven transmembrane domains. GR genes have further been sub-divided into GR subfamilies based on their sequence similarities (Montell, 10 2021). Receptors within those subfamilies tend to respond to taste molecules that are more structurally similar (Kaushik & Kain, 2020). For instance, Gr5a and Gr64f in D. melanogaster are in the same subfamily and respond to sweet taste molecules (Dahanukar et al., 2007; Fujii et al., 2015). On the other hand, in D. melanogaster, a large number of GRs such as Gr32a, Gr33a, and Gr66a respond to bitter compounds (Kaushik & Kain, 2020). 1.2.2.3 Transient receptor potentials Transient receptor potential (TRP) channels are part of a large class of ion channels. There are 13 TRP channels in D. melanogaster (Salgado, 2017). In Ae. aegypti, there are 15 genes encoding TRP channels (Matthews et al., 2016). TRP channels play critical roles in many sensory systems, namely, the sensation of smell, taste, vision, temperature, humidity, mechanical stimuli, intracellular signalling, and ion homeostasis (Salgado, 2017). TRP channels typically contain 6 transmembrane domains and a pore loop (Montell, 2021). TRP channels are classified into 7 subfamilies based on their sequence similarities. These subfamilies include canonical (TRPC), ankyrin (TRPA), vanilloid (TRPV), nompC (TRPN), melastatin (TRPM), mucolipin (TRPML), and polycystic (TRPP) transient potential receptors (Salgado, 2017). D. melanogaster has TRP channels from all of these subfamilies, that serve functions for different sensory systems (Salgado, 2017). For instance, TRPA1 in D. melanogaster, which has a known role in thermosensation, was also found to be expressed in GRNs that respond to aversive tastants. TRPA1 was found to be involved in the aversion to aristolochic acid specifically (Kim et al., 2010). 11 1.2.2.4 Pickpocket receptors Pickpocket receptors (PPKs) are ion channel subunits of the degenerin/epithelial sodium channel (DEG/ENaC) (Freeman & Dahanukar, 2015). PPK receptor proteins are primarily involved in chemosensation and mechanosensation in insects (Latorre-Estivalis et al., 2021). Pickpocket receptors typically contain 2 transmembrane domains and one extracellular domain (Montell, 2021). PPK receptors are classified into seven subfamilies, although not all insect species express members of each of them, showing great variability between species (Latorre-Estivalis et al., 2021). There are 32 genes encoding PPK receptor subunits in Ae. aegypti (Matthews et al., 2019). From phylogenetic analyses, we know that certain PPK receptors are more conserved across multiple insect species, indicating their important function in insects. The ppk28 gene in D. melanogaster, which is involved in the detection of osmolarity, is highly conserved across multiple insect species (Latorre-Estivalis et al., 2021). Water detection serves an important role across insects as it prevents desiccation. Another PPK receptor in D. melanogaster, ppk23, is involved in the detection of pheromones in both sexes, and is also highly conserved (Latorre-Estivalis et al., 2021). Pheromone detection is important in many insect species to control sexual behaviour and promote mating. 1.2.3 Taste coding principles in insects Increasing knowledge of receptors involved in insect taste perception has allowed further study into taste coding principles. Much of the research done on the cellular mechanisms underlying insect taste is from D. melanogaster. Generally, gustatory receptor neurons (GRNs) will express taste receptors that are tuned to specific taste molecules and will elicit a response in that neuron. In D. melanogaster feeding, it seems that specific GRNs express various receptors 12 and respond to taste modalities with the same valence, either positive or negative, and elicit the appropriate behaviour (either feeding or avoidance). This suggests a model for neuronal coding that resembles a labelled line (Kaushik & Kain, 2020). Five distinct classes of GRNs have been defined in D. melanogaster, each denoted based on a defining characteristic. There are sweet GRNs, which respond to sugars, bitter GRNs, which respond to bitter compounds, water GRNs, which respond to low osmolarity, ppk23glut GRNs, which respond to high salt, and Ir94e GRNs, whose function remains unknown (Taruno & Gordon, 2023). In D. melanogaster, sweet GRNs express a range of sugar receptors, including Gr5a and Gr64f, and elicit an appetitive response. On the other hand, bitter neurons respond to a wide range of aversive tastants and elicit an aversive response. The avoidance of some sour compounds is apparently also mediated by a subset of bitter-sensing cells (Charlu et al., 2013; Rimal et al., 2019). Therefore, most taste modalities seem to follow a labelled line coding model, with the exception of salt. All of these GRN classes respond to salt in different ways. Sweet and Ir94e GRNs respond to sodium with a low threshold of activation, and elicit attraction behaviours, whereas bitter and ppk23glut GRNs respond to high salt concentrations and elicit avoidance behaviours (Jaeger et al., 2018). Water GRNs are inhibited by high osmolarity. This indicates that salt taste coding in insects is slightly more complex, and that it is encoded through the combination of many classes of GRNs (Taruno & Gordon, 2023). 1.3 Sexual dimorphism in mosquitoes There are many differences between male and female Ae. aegypti. Structurally, the female is about 1.25 times larger and twice the weight of the male (Christophers, 1960). Both males and females have five tarsomeres that decrease in size towards the distal tip; however, the male’s 13 tarsus is smaller overall than the female’s. The mouthparts of the males and females are also different, as the female is adapted to blood feed, whereas the male is not. The proboscis of the male contains rudimentary mandibles and no distinct hypopharynx, rendering it unsuitable to pierce skin (Christophers, 1960). The anatomy of the neurons expressed in the sensory appendages has not been extensively compared between male and female mosquitoes, specifically for taste. One study compared the expression of widely expressed olfactory co-receptors in the antenna of the male and female Ae. aegypti (Herre et al., 2022). They found that male and female Ae. aegypti had very similar distribution cell bodies and projections of neurons expressing the Orco and Ir25a co-receptors in their antennae. These co-receptors can be expressed in separate or overlapping populations of neurons to form functional receptors for odorant molecules (Herre et al., 2022). Many behaviours are also sexually dimorphic. For instance, females of some mosquito species bite animals in order to get a blood meal, whereas males do not (Christophers, 1960). Females also use their taste system for egg-laying, whereas males do not (Afify & Galizia, 2015). The role of the gustatory system in the detection of non-volatile pheromones is also sexually dimorphic, as each sex is sensitive to pheromones from the opposite sex (Baik & Carlson, 2020). Other behaviours such as nectar feeding are conserved across the sexes. 1.4 Appetitive taste in mosquitoes 1.4.1 Nectar and blood meals in mosquitoes Mosquitoes can survive on a nectar diet since carbohydrates from plant nectar are sufficient for survival. Female mosquitoes of some species, including Ae. aegypti, require a bloodmeal in order to produce eggs for reproduction. Male mosquitoes on the other hand, 14 consume only nectar (Christophers, 1960). Both nectar and blood are therefore appetitive to female mosquitoes and their components elicit a feeding response. Nectar and blood meals serve different functions in females mosquitoes, and were also found to be consumed in different ways. They are detected and consumed by different mouthparts of the female mosquito, the meal size is different, and they are directed to different parts of the body (Gordon & Lumsden, 1939). The mouthparts of the mosquito are housed within a structure called the proboscis. In females, the mouthparts consist of the fascicle, which consists of a bundle of stylets, and the labium that surrounds it. The fascicle is involved in blood feeding, and the labium is involved in nectar feeding. The fascicle consists of the maxillae, mandibles, hypopharynx, and labrum, all of which carry out different roles while they penetrate the skin during the mosquito’s bite (Kong & Wu, 2010). The labrum, which draws the blood from blood vessels, is the only stylet in the fascicle that is innervated by GRNs that will respond to blood (Jové et al., 2020). The labium surrounds the fascicles and helps protect it when the mosquito is not feeding. When blood-feeding, the labium will be retracted to expose the blood-feeding mouthparts, and will rest on the surface of the skin (Kong & Wu, 2010). The labium, which contains the labella on the end, is innervated with GRNs that will respond to nectar. The labium will make contact with the nectar but the fascicles will remain ensheathed (Jové et al., 2020). The labrum is not innervated by neurons that can detect nectar, and the labium is not innervated by neurons that can detect blood, so both of these mouthparts carry out clear distinct functions in regards to feeding (Jové et al., 2020). Although the distinction between the two feeding processes is well described at this level, many questions remain for other sensory appendages. The tarsus of the legs is the first sensory organ that will make contact with a substrate when the mosquito lands, and has been shown to mediate contact behaviours (Dennis et al., 2019). It remains unclear whether taste 15 molecules that initiate either type of feeding elicit responses in overlapping populations of neurons at the level of the tarsus. There have yet to be studies on whether the female mosquito is able to distinguish between meal types with the tarsus alone, or whether the female only detects a positive valence, which results in a general appetitive behaviour. 1.4.2 Sucrose, lactic acid, and sodium chloride The major sugars found in most plant nectar are sucrose, glucose, and fructose (Wolff, 2006). In D. melanogaster, they are detected by different receptors, but all elicit positive appetitive responses (Jiao et al., 2007; Miyamoto et al., 2012). When presented to the labium of Ae. aegypti females, all three sugars are ingested, indicating that they are appetitive (Jové et al., 2020). In this study, we use sucrose as a proxy for nectar. For the initiation of blood feeding, anthropophilic mosquitoes such as Ae. aegypti will also have an appetitive response to components of human sweat or skin. Two tastants associated with sweat and skin are lactic acid and low concentrations of sodium chloride (NaCl) (Ament et al., 1997; Klous et al., 2021; McSwiney, 1934). Both elicit appetitive responses in D. melanogaster (Jaeger et al., 2018; Stanley et al., 2021) and mosquitoes (Acree et al., 1968; Jové et al., 2020). The pathways at the level of the tarsus that respond to these tastants remain unclear in mosquitoes. This opens up questions about whether the GRNs in the tarsus responding to sucrose might also respond to some components of sweat and skin, leading to a non-specific feeding response. In D. melanogaster, many studies have investigated the expression of receptors responding to appetitive cues. Interestingly, several types of appetitive cues appear to activate subsets of sweet GRNs. Ir56b is expressed in a sub-population of sweet GRNs, where it mediates 16 detection of sodium with a low threshold of activation (Dweck et al., 2022). Ir56d is similarly required in a subset of sweet GRNs to mediate attraction to fatty acids (Ahn et al., 2017). Finally, lactic acid also elicits a positive appetitive response in flies by activating a sub-population of sweet GRNs, which partially requires the co-receptor Ir25a (Stanley et al., 2021). 1.5 Taste responses in Aedes aegypti mosquitoes There are multiple techniques to study the gustatory system and taste responses in mosquito sensory neurons. Electrophysiological recording and calcium imaging can be used to study responses in GRNs. Other molecular techniques can be employed to manipulate the genes involved in these processes and study their functions. 1.5.1 Sensilla electrophysiology recordings in the mosquito Electrophysiology can be used to record the sensitivity of taste neurons in a sensillum. Typically, an electrode with a tastant will be placed at the tip of a sensillum. The tastant will come in contact with the GRNs in that sensillum and the electrode can record the activity in the neurons. The identity of the neurons can be distinguished based on their amplitude and the shape of the spike (Delventhal et al., 2014). Electrophysiological recordings have been commonly done in studies of olfaction and gustation in mosquitoes. For instance, in olfaction studies, single sensillum recordings on the maxillary palp of the mosquito have revealed a population of neurons that is sensitive to carbon dioxide, a known cue for host-seeking (Grant et al., 1995). In gustation studies, electrophysiological recordings of sensilla on the labellum of Aedes mosquitoes have revealed separate gustatory pathways for appetitive and aversive tastants (Sanford et al., 2013). The 17 amplitude and spike patterns of GRNs on the labella in response to salt, sugars, and bitters were evaluated and three types of GRNs responding to each tastant individually were characterized (Sanford et al., 2013). 1.5.2 Calcium imaging of sensory neurons Another commonly used technique in studying responses in GRNs is calcium imaging. Typically, a calcium indicator will be expressed in a GRN population that expresses a receptor of interest. When there is activity in the neurons and an influx of calcium in the cell, there will be increased fluorescence of the calcium indicator. By measuring this fluorescence over time, we can track the changes in activity of the cell. This technique allows for targeting of specific cells (Wei et al., 2020). Calcium imaging has become prominent in studying the cellular and molecular basis of gustation in insects. One study used calcium imaging to investigate the role of the receptor Ir7a and Ir7f in blood perception in the female Ae. aegypti stylet (Jové et al., 2020). With this technique, they showed that neurons expressing Ir7a and Ir7f are localized to the stylet of female mosquitoes, and that most respond to blood. They report that almost all Ir7a-expressing neurons and a subpopulation of Ir7f-expressing neurons show a response to blood, as well as a mixture of some components of blood (Jové et al., 2020). Another study of interest used calcium imaging to measure the response of ppk301-expressing cells when exposed to different sodium chloride osmolarities (Matthews et al., 2019). In D. melanogaster, the ppk28 receptor was identified as responding to pure water or low osmolarity in GRNs (Cameron et al., 2010). The ortholog of ppk28 in Ae. aegypti was found to 18 be ppk301, and cells expressing ppk301 respond to low osmolarity water in a ppk301-dependent fashion, as well as higher osmolarity salt water independently of ppk301 (Matthews et al., 2019). 1.5.3 Molecular techniques to study the gustatory system Using other molecular techniques, there is an array of possibilities to study taste neurons at the molecular level. Many genetic tools are employed to manipulate genes and study their function in gustation. Some common genetic techniques used in the field include chemogenetic activation by ectopically expressing receptors in cells that they are normally not expressed in, optogenetically stimulating neurons expressing certain genes, or mutagenesis to introduce a loss-of-function mutation in a gene, to name a few. One way to study the function of a gene is to artificially activate cells expressing it. One common technique to do this is chemogenetics. One study used this molecular techniques to investigate the role of Gr4-expressing cells in sweet perception (Jové et al., 2020). Ae. aegypti Gr4 is an ortholog of the D. melanogaster sugar receptor genes Gr5a and Gr64f (Kent & Robertson, 2009). The detection and subsequent ingestion of sweet foods is important in many insect species due to its high nutritional value. Gr4-expressing cells were found to be involved in nectar feeding behaviour in Ae. aegypti mosquitoes (Jové et al., 2020). Ectopic expression of a capsaicin receptor in cells that express Gr4 generates an appetitive behavioural response to capsaicin as a result. This supports the role of Gr4 cells in appetitive feeding (Jové et al., 2020). Another common way to artificially activate cells expressing a specific receptor is optogenetic stimulation. This involves the use of light to activate specific neuron populations. For instance, optogenetic activation of carbon dioxide sensing neurons leads to changes in host-seeking behaviours in Ae. aegypti. Brief pulse activations, which are perceived as CO2 to the 19 mosquito, result in a persistent host-seeking behaviour in non-blood-fed females (Sorrells et al., 2022). Other studies may introduce loss-of-function mutations in genes of interest in order to study their function. For instance, one study generated Ae. aegypti ppk301 mutant mosquitoes, which lack the ppk301 gene (Matthews et al., 2019). This genetic reagent supports the involvement of ppk301 in the response to low osmolarity solutions, but indicates that the cells expressing ppk301 respond to high salt via a different pathway (Matthews et al., 2019). This study found that ppk301 was vital to finding an appropriate freshwater egg-laying site and is heavily involved in oviposition behaviour (Matthews et al., 2019). 1.6 Insect neuroanatomy in the tarsi As of recently, more studies are exploring the anatomy of GRNs in the mosquito’s periphery. One recent study investigated the expression of some important populations of gustatory neurons in the Ae. aegypti mosquito (Morita et al., 2023). They characterized all neurons in the most distal tarsomere of all legs of female mosquitoes, along with populations of neurons expressing specific receptors. They report that the number of neurons is highest in the foreleg, and lowest in the hindleg. This pattern is also consistent in widely expressed co-receptors but less so in populations of neurons responding to sugars. They also describe the arrangement of these neurons in the tarsi, reporting two large planes on the cranial and caudal sides of the tarsus (Morita et al., 2023). Another study in mosquitoes investigated the expression of all neurons in the sensory appendages of the mosquito species Anopheles coluzzii (Konopka et al., 2023). Of interest here, is their exploration of all neurons in the most distal tarsomere on all three legs of the female 20 mosquito. Similarly to Ae. aegypti, they report highest numbers of neurons in the foreleg, and lowest in the hindleg, however, describe lower overall numbers (Konopka et al., 2023). This provides a comparison for Ae. aegypti that is evolutionarily closer than the fruit fly. Similar studies mapping the expression of GRs in the D. melanogaster tarsus, found that expression of various taste receptors is slightly different in the foreleg compared to the other legs, but similar in the mid and hind legs. They also report that most GR-expressing cells do not show sexual dimorphism in the D. melanogaster tarsus (Ling et al., 2014a). 1.7 Technical details 1.7.1 Q-system driver-reporter construct The Q-system is a binary expression system that offers many possibilities for genetic manipulations in mosquitoes (Riabinina et al., 2015). It consists of two core components, the driver, and the reporter. The driver consists of a transgene with the QF2 transcriptional activator sequence in a gene of interest (Figure 1.3). The reporter consists of a transgene with the QF2 binding site, QUAS, and effectors, which may be fluorescent proteins (Figure 1.3). The system is designed to keep the driver and reporter components in different genetic lines of the animal, so that when they are crossed together, their progeny will express the effectors into the cells that express the gene of interest. This also allows a greater number of options, as different combinations of driver and reporter lines can be crossed. There is an additional suppressor component, QS, that can be added to suppress the transcriptional activator QF2. Additionally, the QS suppressor can in turn be suppressed by quinic acid (Riabinina & Potter, 2016). The Q-system was originally conceived as a tool for the D. melanogaster model (Riabinina et al., 2015). In D. melanogaster, the standard system for binary expression tends to be the Gal4/UAS system. 21 The Q-system was originally designed to overcome some limitations of the Gal4/UAS system, mainly to change the activity of the repressor without changing the ambient temperature (Riabinina & Potter, 2016). The two systems can also be used together for more extensive genetic manipulations. In Ae. aegypti, the Gal4/UAS system has failed to work as well as it does in the fruit fly (Zhao et al., 2021). The Q-system has now been adopted in Ae. aegypti mosquitoes, and has become the primary binary system, specifically in neurobiology, offering a range of possibilities for genetic manipulation (Fölsz et al., 2022). For instance, in the last few years, this system has been used to study olfactory co-receptors (Herre et al., 2022), pan-neuronal expression (Zhao et al., 2021), blood-feeding sensory neurons (Jové et al., 2020), and freshwater oviposition sensory neurons (Matthews et al., 2019) to name a few. For easy identification of the individuals with the transgene in the lab, driver lines contain a 3XP3-dsRed marker, which expresses a red fluorescent protein in the eye of the mosquito. The reporter lines contain a 3XP3-ECFP marker, which expresses a green fluorescent protein in the eye. Figure 1.3 – Schematic of Q-system Driver component consists of the gene of interest (blue) with the transcription factor QF2 (green) embedded in its sequence. The reporter component consists of the QF2 binding site QUAS (red), and the effectors (yellow). QF2 binds to QUAS to initiate transcription of the effectors in cells expressing the gene of interest. 22 1.7.2 GCaMP GCaMP6s, the effector in the reporter line used here, is a genetically encoded calcium indicator. GCaMP is the result of the fusion between a green fluorescent protein (GFP) and calmodulin, a calcium binding protein. When calcium levels rise in a cell, calmodulin binds to the calcium, resulting in an increase in GFP fluorescence (Carter et al., 2022). When imaging with a GCaMP effector, the mean fluorescence of a cell can therefore be a proxy for the neuronal activity. GCaMP6s specifically is a variant of GCaMP that has an ultrasensitive calcium sensor, even for small numbers of action potentials, specifically in mice, zebrafish and flies, and is brighter than previous variants (T.-W. Chen et al., 2013). The change in mean fluorescence from the onset of a stimulus is then compared to the mean fluorescence at baseline, which gives the F/F value. The peak F/F value is the largest change in mean fluorescence from baseline observed during a stimulus delivery. 1.8 Experimental objectives Despite more recent studies describing the gustatory system in Ae. aegypti mosquitoes, there are still large knowledge gaps in our understanding of mosquito taste. The anatomy of different populations of GRNs, as well as the functional responses in the tarsus of the mosquito, remain understudied. The general aim of this thesis is to describe the anatomy of various populations of GRNs that are key to important behaviours, and to develop a protocol for studying the response of those populations to different taste stimuli. Overall, this will increase our knowledge of the neural circuitry involved in taste processing in Ae. aegypti which could inform future studies of this system. The goal of this thesis is to generate background about the cells involved in taste processing and produce a protocol for studying the function of those cells. 23 1.8.1 Anatomy of the mosquito taste system The first aim of this thesis is to characterize the density and position of GRNs that express certain receptors. I focus on the anatomy of all neurons, using a pan-neuronal marker. Additionally, I describe the populations of neurons expressing Ir25a, Gr4, and ppk301. Using the Q-system, a GFP fluorescent marker will label all cell populations of interest. This allows me to characterize the anatomy of all of these populations in the mosquito’s tarsus. I aim to understand the organization of these neurons in the five tarsomeres of the tarsus, in all three legs, and relate this to the use of each leg in various behaviours. I also aim to compare these populations between males and females to draw conclusions on their role in sexually dimorphic behaviours. Due to the female mosquito being larger in size, and performing sexually dimorphic gustation-driven behaviours (Christophers, 1960), I expect larger populations of neurons involved in taste perception in the female tarsus. Some receptors that I focus on have previously been shown to be involved in specific behaviours. Neurons expressing GR4, for instance, are involved in nectar-feeding behaviour (Jové et al., 2020), which is performed by both sexes, and so I may expect this population of neurons to be less sexually dimorphic. PPK301 on the other hand, was shown to be involved in egg-laying behaviour (Matthews et al., 2019), a behaviour only performed by females, and so I may expect that it would be enriched in females. For all populations of neurons, I may also expect a higher density of neurons in the more distal tarsomeres, seeing as they make more physical contact with the surface. 1.8.2 Functional profiling of the mosquito taste system The second aim of this thesis is to develop a protocol to measure the response of individual neurons to various taste stimuli. Here, I can use the Q-system to label a population of 24 neurons with the calcium indicator, GCaMP. This allows me to track the activity of cells in that population during the delivery of a stimulus. Once this protocol was working, I investigated whether cells in the tarsus that respond to sucrose, a proxy for nectar, would also respond to other appetitive tastants such as lactic acid and low concentrations of salt, which are components of sweat and blood. I expect that female Ae. aegypti mosquitoes will respond to nectar and blood feeding cues in overlapping populations of neurons in the tarsus, and that Ae. aegypti males will not respond to blood-feeding cues. 25 Chapter 2: Methods 2.1 Aedes aegypti mosquito strains Various genetic lines of mosquitoes were used in this project, see Table 2.1 for reference. Table 2.1. Aedes aegypti genetic strains Genotype Category Origin ppk301-QF2, 3XP3-dsRed QF2 Driver Vosshall Lab (Matthews et al., 2019) Ir25a-QF2, 3XP3-dsRed QF2 Driver Vosshall lab (Herre et al., 2022) Gr4-QF2, 3XP3-dsRed QF2 Driver Vosshall lab (Jové et al., 2020) brp-QF2, 3XP3-dsRed QF2 Driver McBride lab (Zhao et al., 2021) QUAS-CD8:GFP, 3XP3-ECFP QUAS Reporter Vosshall lab (Matthews et al., 2019) QUAS-dTomato-T2A-GCaMP6s, 3XP3-ECFP QUAS Reporter Vosshall lab (Matthews et al., 2019) brp-QF2 Yellow, 3XP3-dsRed Yellow QF2 Driver McMeniman lab (unpublished) QUAS-GCaMP6s Yellow, 3XP3-ECFP Yellow QUAS Reporter McMeniman lab (unpublished) 2.2 Mosquito rearing and maintenance Ae. aegypti transgenic mosquito strains were reared at 26C. Mosquito larvae were hatched in small cups with dechlorinated water using a vacuum pump for 1-2 hours to remove oxygen. They were then transferred to larger trays (dimensions 12.75  10.4375 cm) with dechlorinated water and fed with agar-fish food cubes and ground fish food (Tetramin 77803-02). Pupae were then transferred to small cups that were placed in BugDorm mesh or hard plastic cages of 30  30  30 cm dimensions until they emerged as adults. Adults were kept on a 26 photoperiod of 14-hour light and 10-hour dark. Adult mosquitoes had constant access to 10% sucrose solution, and were kept at 27C, with 70-75% humidity. For standard strain maintenance and making crosses, adult female mosquitoes were blood-fed with sterile, defibrinated sheep blood on an artificial feeder set at 37.5C. A cup with around 1 inch of dechlorinated water, lined with filter papers, was placed in the cage for females to lay eggs on. Once eggs were laid, filter papers were removed from the cage and dried. They were used no more than 4 months after being laid. For crosses between a driver and reporter line, eggs from each strain were hatched and larvae were sorted under a fluorescent dissecting scope based on whether they expressed the appropriate fluorescent protein in the eye. Driver lines have a red fluorescent protein on the 3XP3 eye marker, and the reporter lines have a green fluorescent protein on the 3XP3 eye marker (Figure 2.1). Pupae were then separated by sex and put into different cages. For sorting of the pupae, sexes are distinguished by size and shape. Adult males from one strain and females from another were then placed into the same cage. Transportation of adult mosquitoes was done by mouth aspiration. Blood feeding was repeated multiple times over the course of 4-5 weeks to maximize egg yield. Progenies were further sorted to ensure that they expressed both eye markers, indicating that they contained both the driver and reporter components. 27 Figure 2.1 – Fluorescence in mosquito larvae 3XP3-dsRed marker in driver line, showing red eye fluorescence (A), and 3XP3-ECFP marker in reporter line, showing green eye fluorescence (B). White arrowhead indicates eye. Red fluorescence in pan-neuronal expression pattern (C). 28 2.3 Anatomical data methods 2.3.1 Mosquito strains Transgenic progenies of driver and reporter crosses were used. Four different driver lines were used, all crossed to the same reporter line. The driver lines ppk301-QF2, Ir25a-QF2, Gr4-QF2, and brp-QF2 were used, all crossed to the reporter line QUAS-CD8:GFP for data acquisition (Table 2.1). CD8:GFP is a membrane-bound fluorophore that allowed for easier distinction of the cell bodies, and therefore more accurate counting. The reporter line QUAS-dTomato-T2A-GCaMP6s was used for troubleshooting purposes but was not used for the data presented here. All mosquitoes used for imaging were between 7 and 14 days after pupation. 2.3.2 Sample preparation Transgenic mosquitoes were placed on ice to anesthetize them for dissection. Using forceps, the leg was plucked from the mosquito and placed in 100% ethanol for 50 seconds. The legs were then placed on double sided tape, and with the use of a paintbrush, the scales were brushed off. A SS1X9-SecureSeal™ imaging spacer (catalog number GBL654002-100EA) with a 9 mm diameter and 0.12 mm depth was put on a 75 mm by 25 mm, and 1mm thick microscope slide. 20 L of glycerol was used in the spacer. The leg was placed in the glycerol and a 22x22 mm microscope cover glass was placed on top of the spacer (Figure 2.2). A drop of Zeiss Immersol™ G (catalog number 462959-9901-000) was used for objective immersion. 29 Figure 2.2 – Anatomical imaging sample preparation Female mosquito tarsus (A) which is plucked from mosquito on ice (B). Tarsus is placed in glycerol on microscope slide (C). 2.3.3 Confocal imaging Confocal imaging was performed on a Zeiss LSM 900 with Airyscan 2 microscope. Images were acquired using the Airyscan 2 detector in confocal mode. Imaging was done using the EGFP channel with an excitation wavelength of 488, and an electronically switchable illumination and detection module (ESID) channel. ESID combines the transmitted light illumination and the detection so that no mechanical change needs to happen during acquisition. 30 Images were acquired with an LD LCI plan-apochromat 25x/0.8 with a 2.51 airy units (AU) pinhole dipped in Zeiss Immersol™ G on the cover-glass. Scan zoom for images ranged between 0.65-2.2x, depending on the leg and segment. A z-stack through the leg was acquired with a 2 m step size. 2.4 Functional data methods 2.4.1 Mosquito strains Transgenic progenies of driver and reporter crosses were used. The driver line is the yellow mutant brp-QF2, and the reporter line is the yellow mutant QUAS-GCaMP6s (Table 2.1). Additional lines were used for troubleshooting purposes but were not used for the data presented here. All mosquitoes used for imaging were between 7 and 14 days after pupation. 2.4.2 Inverted widefield fluorescence imaging Images were collected on a Zeiss Axio Observer Live Cell Imaging System equipped with a LED based Colibri excitation source. Fluorophores were excited with 493 nm and emission was collected in these 460-488 wavelength ranges with a LD plan-neofluar 20x/0.4 objective lens. An exposure time of 150ms resulted in an acquisition rate of approximately 6.6 frames per second. 2.4.3 Sample Preparation Mosquitoes were placed on ice for anesthetization. One leg was plucked and the detached end was placed in a drop of Molykote 111 compound silicone grease on a 22 x 40 mm micro 31 cover glass. This was placed in a custom-made 3D-printed stage adaptor. A Warner Instrument RC-26GLP imaging chamber was placed on top of the sample and a Warner Instrument SHD-26GH/10 slice anchor was used to hold down the sample (Figure 2.3). Figure 2.3 – Functional imaging setup Sample is placed on coverslip. Imaging chamber and slide anchor placed on top. Perfusion system delivers liquid tastants to the sample. 2.4.4 Sucrose cell experiment protocol Yellow brp>GCaMP6s transgenic Ae. aegypti mosquitoes were used (Table 2.1). Liquid tastant stimuli were delivered (at an approximate flow rate of 10-15 L/s) to the imaging chamber using a ValveBank8 Perfusions system and controller, and an in-house vacuum system was used to suction out liquid. 32 Stimuli were delivered in the following sequence: 100 mM sucrose, then randomize either 100 mM lactic acid or 100 mM NaCl, followed by the other, then 100 mM sucrose again. Prior to stimuli delivery, the sample was kept in water that was purified using a Millipore MilliQ system for one minute. Each stimulus was delivered for 30 seconds and had a wash out period with MilliQ water of 3 minutes between them. The mean fluorescence of the cell bodies will be used as a proxy for neuronal activity. The mean fluorescence of the cell during the stimulus delivery is compared to the mean fluorescence of the cell during baseline, which is immediately preceding the stimulus. This gives the F/F values for each timepoint. In this experiment, cells were quantitively scored as responding if they show a change in F/F at the onset of a stimulus delivery. They were scored as sucrose-responding cells if they show a response at both sucrose deliveries, or sodium chloride-responding cells or lactic acid-responding cells, or any combination of the three. 2.4.5 Sucrose cell experiment inclusion requirements Not all imaging runs that were acquired for the sucrose cell experiment were included in the final dataset. There were strict rules for inclusion into the dataset to ensure that the dataset presented is representative of real changes in cell activity: 1) Sample does not move during imaging One requirement for a run to be included in the dataset was that the sample did not move out of frame or out of focus. In some runs, the leg would completely shift during imaging, which prevents us from tracking specific cell bodies throughout all stimuli deliveries. 33 2) Apparent response in cells Keeping in mind that I was using a pan-neuronal driver here, I would not expect all cells to respond to the stimuli presented in this experiment. Many runs presented no obvious problem, but cells did not show responses to any of the stimuli delivered. 3) No large fluctuation in fluorescence outside stimuli windows Some runs presented cell bodies with large fluctuations in fluorescence outside of stimuli delivery time periods. This could indicate that the cell was responding to something other than the intended stimulus, or that it may be moving in and out of focus. This hints at an unreliable response from the cell, and these runs were excluded from the final dataset. Only runs in which the rise in fluorescence started after the onset of the delivered stimulus and within the stimulus delivery window were considered. 4) No apparent disturbances The final criterion for inclusion in the dataset was that the run did not have any apparent disturbances. For example, an air bubble passing around the sample and disturbing the imaging. Air bubbles create optical disturbances and cause a change in mean fluorescence that is not associated with a change in activity. An air bubble could also cause a mechanosensory response in a neuron that is different than a response to the given stimuli. 34 2.5 Data analysis All image processing was done using Fiji/ImageJ, version 2.14.0/1.54f (Schindelin et al., 2012; Schneider et al., 2012). Data processing was done using Microsoft Excel, GraphPad Prism 10, and Python. Image processing was done using Adobe Illustrator 2023. For the anatomical data, cell bodies were counted manually in Fiji/ImageJ with the multi-point function, by carefully scrolling through each slice of the z-stack. For the functional data, and sucrose cell experiment data, the region of interest (ROI), was drawn around a cell body based on the maximum projection of all time frames. The baseline fluorescence for F/F calculation was calculated based on the average mean fluorescence of 10 frames prior to each stimulus delivery, and the single peak F/F value for each stimulus was calculated based on its respective baseline. 2.6 Statistical analysis All statistical analyses were performed using GraphPad Prism 10. Details of individual statistical analyses and sample sizes are provided in figure legends. For anatomical data, replicates are different legs from different individuals. For functional data, replicates are different individual cells. 2.7 Data availability Data available upon request. Lead contact: Benjamin Matthews (ben.matthews@zoology.ubc.ca). 35 Chapter 3: Results 3.1 Anatomy study The neuroanatomy of the tarsi of Ae. aegypti has not been well described in the past. Here, I developed a protocol to characterize the anatomy of four populations of neurons in the tarsi of the mosquitoes. I utilized the Q-system to label different cell populations with the membrane-bound fluorophore CD8:GFP. I utilized four driver lines that targeted cell populations expressing receptors of interest in gustation. I used a pan neuronal bruchpilot (brp) driver line, Ir25a driver line, Gr4 driver line, and ppk301 driver line. I acquired images through each of the 5 tarsomeres of all three legs of both males and females for the different driver lines. Five replicates were acquired for each leg. 3.1.1 Driver comparisons I acquired confocal z-stack images of GFP expression through the whole tarsus of the foreleg, midleg, and hindleg of males and females. One representative maximum intensity projection of this expression is overlayed with ESID for the fifth, most distal tarsomere (Figure 3.1). From this, I observed interesting patterns of expression in each driver line. The pan neuronal expression revealed two large planes of high density of cell bodies on either side of the leg. This is consistent with observations from another study (Morita et al., 2023). Denser populations, such as Ir25a and even Gr4 also show more clustering, with rare instances of individual isolated cells, whereas not much clustering was observed for ppk301 neurons. 36 Figure 3.1 – Confocal images of fifth tarsomere Maximum intensity projection of confocal z-stack images of GFP expression in the fifth tarsomere of brp-QF2>QUAS-CD8:GFP (A), Ir25a-QF2>QUAS-CD8:GFP (B), Gr4-QF2>QUAS-CD8:GFP (C), ppk301-QF2>QUAS-CD8:GFP (D). These are overlayed with the ESID channel. Top three rows are female fore, mid, and hind legs respectively. Bottom three rows are male fore, mid, and hind legs respectively. Scale bar is 25 m for all panels. Cells were then counted manually, while carefully scrolling through the z-stack, and total cell counts of the legs is reported for each driver in females (Figure 3.2A) and males (Figure 3.2B). In both males and females, the pan-neuronal marker shows the greatest number of cells across all legs. In females, I counted 353.2  22 (mean  SEM), 276.6  30.7, and 214  26.8 cells expressing brp in the fore, mid, and hind legs respectively. In males, I counted 244.6  23, 267.6  14.2, and 217  12.4 cells expressing brp in the fore, mid, and hind legs respectively. This is consistent with expectations since brp labels all neurons, and expression of Ir25a, Gr4, and ppk301 will only be in sub-populations. Ir25a-expressing neurons represent the second 37 greatest population I observed. In females, I counted 244.6  10.6, 181.2  2.7, and 109.6  7.1 cells expressing Ir25a in the fore, mid, and hind legs respectively. In males, I counted 156.8  10.4, 140.6  6.9, and 92  8 cells expressing Ir25a in the fore, mid, and hind legs respectively. Again, this is in line with expectations since Ir25a is a widely expressed co-receptor that works with other taste receptors to detect a variety of taste molecules. Populations of neurons expressing Gr4 and ppk301 are the smallest, as is expected since they are predicted to respond to specific taste molecules. In females, I counted 67.8  2.4, 57.8  0.8, and 50.6  1.8 cells expressing Gr4 in the fore, mid, and hind legs respectively. In males, I counted 61.4  4.8, 52  2.3, and 47  2.8 cells expressing Gr4 in the fore, mid, and hind legs respectively. In females, I counted 30.4  2.1, 32.4  2, and 31.2  2.4 cells expressing ppk301 in the fore, mid, and hind legs respectively. In males, I counted 25  1.1, 25.6  1.6, and 22.4  1.2 cells expressing ppk301 in the fore, mid, and hind legs respectively. 38 Figure 3.2 – Whole tarsi cell count comparison between different driver lines Total cell count of whole tarsi on foreleg, midleg, and hindleg for brp-QF2>QUAS-CD8:GFP (blue), Ir25a-QF2>QUAS-CD8:GFP (red), Gr4-QF2>QUAS-CD8:GFP (green), ppk301-QF2>QUAS-CD8:GFP (purple) in females (A) and males (B). Bar represents mean with SEM. N = 5. 39 3.1.2 Sex and leg comparisons I carried out a two-way analysis of variance (ANOVA) with Tukey’s multiple comparison test to compare the total cell counts between the three different legs within each sex and between males and females for each leg (Figure 3.3). Comparison is only shown for significant differences on figure. Cell counts are the same as reported in section 3.1.1. In the pan neuronal population, females show a higher cell count compared to males in the foreleg, but similar cell counts in the mid and hind legs (Figure 3.3A). In females, across the fore, mid, and hind legs, I observed the largest population in the foreleg, and the lowest in the hindleg (Figure 3.3A). In males, this pattern is not observed, and all legs show similar cell counts (Figure 3.3A). In the Ir25a-expressing cells, females show significantly more cells in the foreleg and midleg compared to males (Figure 3.3B). In females, the foreleg contains the largest population of Ir25a-expressing cells, and the hindleg contains the smallest (Figure 3.3B). The male foreleg and midleg contain similar populations, whereas the hindleg contains a smaller population. In the Gr4-expressing cells, there are no significant differences in the number of cells between males and females in any of the legs (Figure 3.3C). Both males and females contain a significantly larger population of Gr4-expressing cells in their foreleg compared to the hindleg (Figure 3.3C). In the ppk301-expressing cells, females seem to have slightly larger populations than males, however this difference is only significant in the hindleg (Figure 3.3D). Both males and females also show similar populations of ppk301-expressing neurons across all three legs (Figure 3.3D). 40 Figure 3.3 – Whole tarsi cell count comparison of legs and sexes Total cell count of whole tarsi on foreleg, midleg, and hindleg for brp-QF2>QUAS-CD8:GFP (A), Ir25a-QF2>QUAS-CD8:GFP (B), Gr4-QF2>QUAS-CD8:GFP (C), ppk301-QF2>QUAS-CD8:GFP (D) in females (purple) and males (blue). Bar represents mean with SD. N = 5. Two-way ANOVA with Tukey’s multiple comparison test, * indicates p-values less than or equal to 0.05. 41 3.1.3 Segment comparisons I counted the total amount of cells in each of the 5 tarsomeres of the leg in males and females with a brp driver (Figure 3.4), Ir25a (Figure 3.5), Gr4 (Figure 3.6), and ppk301 (Figure 3.7). The fifth tarsomere is the most distal, at the tip of the leg, with the claw, and the first tarsomere is the most proximal, attached to the tibia. I observed that the difference in total cell count across the tarsomeres was smaller, but that the difference in density of cells was larger, primarily driven by the relative size of the tarsomeres. To accentuate the density of the cell populations across the different tarsomeres, I divided the total cell count by the length of each tarsomere and plotted this measure of cell density for each driver (Figures 3.8-11). The segments were compared using the non-parametric Friedman test with Dunn’s multiple comparison test. Only significant changes are indicated on the graphs. 42 Figure 3.4 – Pan neuronal total cell count Total cell count in all five tarsomeres of brp. Female (left) and male (right) counts in foreleg (top), midleg (middle), and hindleg (bottom). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). Only significant differences shown. 43 Figure 3.5 – Ir25a total cell counts Total cell count in all five tarsomeres of Ir25a. Female (left) and male (right) counts in foreleg (top), midleg (middle), and hindleg (bottom). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). Only significant differences shown. 44 Figure 3.6 – Gr4 total cell counts Total cell count in all five tarsomeres of Gr4. Female (left) and male (right) counts in foreleg (top), midleg (middle), and hindleg (bottom). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). Only significant differences shown. 45 Figure 3.7 – ppk301 total cell counts Total cell count in all five tarsomeres of ppk301. Female (left) and male (right) counts in foreleg (top), midleg (middle), and hindleg (bottom). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). Only significant differences shown. 46 In the pan neuronal driver, I observe an increasing density of cells towards the more distal tarsomeres (Figure 3.8). In females, both the foreleg and midleg show a step-wise decrease towards the more proximal tarsomeres (Figure 3.8A-B). The pattern is somewhat similar in males, except that the densest tarsomere in the foreleg and midleg is the fourth (Figure 3.8D-E). The hindleg in both sexes shows a vastly denser fifth tarsomere with similar densities in the other segments (Figure 3.8C,F). In the Ir25a driver, I observe a very similar pattern of cell density to that of the pan neuronal driver (Figure 3.9). In the Gr4 driver, the pattern of density across the tarsomeres in females and males is still similar to that of the pan neuronal driver (Figure 3.10). I still observe a larger density of Gr4-expressing neurons in the fifth tarsomere of the hindleg, but a much lower density compared to the fore and mid legs. One study by Morita et al. (2023) counted the cell bodies in the fifth tarsomere of females for the brp, Ir25a, and Gr4 driver lines. They used a different reporter line, driving the expression of a different fluorophore. They reported very similar, if not slightly lower, cell counts to this analysis. In the fifth tarsomere of females, they report an average of 75 neurons in the foreleg, 62 neurons in the midleg, and 47 neurons in the hindleg (Morita et al., 2023). In the fifth tarsomere of females, we report an average of 85 neurons in the foreleg, 69 neurons in the midleg, and 55 neurons in the hindleg (Figure 3.4). In the ppk301 driver, male and female forelegs and midlegs both show a less pronounced decrease in cell density towards the more proximal tarsomere (Figure 3.11A,B,D,E), and the hindleg in both sexes show a more even density across segments (Figure 3.11C,F). 47 Figure 3.8 – Pan neuronal segment densities Total cell count over length in m for all five tarsomeres of female foreleg (A), midleg (B), and hindleg (C), and male foreleg (D), midleg (E), and hindleg (F). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). 48 Figure 3.9 – Ir25a-expressing cells segment densities Total cell count over length in m for all five tarsomeres of female foreleg (A), midleg (B), and hindleg (C), and male foreleg (D), midleg (E), and hindleg (F). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). 49 Figure 3.10 – Gr4-expressing cells segment densities Total cell count over length in m for all five tarsomeres of female foreleg (A), midleg (B), and hindleg (C), and male foreleg (D), midleg (E), and hindleg (F). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). 50 Figure 3.11 – ppk301-expressing cells segment densities Total cell count over length in m for all five tarsomeres of female foreleg (A), midleg (B), and hindleg (C), and male foreleg (D), midleg (E), and hindleg (F). N = 5. Friedman test with Dunn’s multiple comparison test ( = 0.05). 51 3.2 Functional profiling of the taste system in mosquito tarsi We developed a novel protocol for evaluating neuronal responses of individual cells to various liquid taste stimuli. This protocol enables us to perform live cell imaging in the tarsus of mosquitoes during the delivery of various liquids. Once again, I can use the driver/reporter construct to label a cell population of interest with a GCaMP effector. This allows us to track the neuronal activity of the individual cells. 3.2.1 Proof that setup works Many steps were undertaken in the process of troubleshooting the functional imaging setup. In short, I was initially using an upright microscope, with an immersion objective dipped in the tastant solution. This resulted in many problems, including the obstruction of the objective and other optical disturbances during stimulus delivery. This led me to using the inverted microscope, with which the objective is imaging from below the coverslip. I was also previously using transgenic mosquitoes in a wild-type background. Since the scales on the tarsus were quite dark, I had to remove them in order to visualize the cell bodies. This caused damage to the legs which might have prevented me from seeing responses in the legs. I therefore moved on to using the transgenic mosquitoes in a yellow mutant background, which has a lighter cuticle, so that no scale removal had to be done (Li et al., 2017). This led to the development of the functional imaging setup that I used for the sucrose cell experiment (see Section 3.2.2). With this setup, I was able to obtain responses from individual cells to various tastant stimuli (Figure 3.12). I found cells responding to sucrose (Figure 3.12A), lactic acid (Figure 3.12B), and sodium chloride (Figure 3.12C). For our purposes, a response is characterized as an elevation from baseline of GCaMP signal that is 52 temporally correlated to a stimulus. I observed responses with an amplitude ranging from 20-200% increases in signal from baseline, which falls within the scope of responses that have previously been observed in flies and mosquitoes (Jaeger et al., 2018; Jové et al., 2020; Matthews et al., 2019; McDowell et al., 2022; Stanley et al., 2021). A small delay of approximately five to ten seconds in the response after the onset of the stimulus is expected, and correlated with the time it takes for the new liquid to pass through the delivery tube, into the imaging chamber. 53 Figure 3.12 – Proof of principle responses in functional setup Mean trace responses with SEM to 250 mM sucrose delivery (A), 250 mM lactic acid (B), and 150 mM NaCl (C). Red bar indicates tastant delivery period. Sample size (N) is indicated on each graph. 54 3.2.2 Sucrose cell experiment Here I used the yellow mutant brp-QF2>QUAS-GCaMP6s line to drive expression of the effector GCaMP6s in all neurons. I investigated whether cells that responded to 100 mM sucrose would also respond to other appetitive tastants, namely 100 mM lactic acid and 100 mM NaCl. I carried out many runs, with only some showing cells responding to any of the tastants delivered (see section 2.4.5). I observed responses to the three tastants tested in non-overlapping populations of neurons, meaning that cells responding to sucrose did not respond to lactic acid or salt. Based on the inclusion requirements outlined for this experiment in Section 2.4.5, for a total of 158 runs performed, only 24 runs were used in the final dataset. 35 runs showed movement of the sample during imaging, 66 runs did not show any apparent responses to the stimuli, 28 runs showed large fluctuations in mean fluorescence outside of the stimuli windows, and 5 runs showed apparent disturbances during imaging. None of these were included in the final dataset. Notably, the kinetics of the GCaMP responses appeared to be different between tastants. I traced the F/F percent value of the cells before, during, and after stimulus delivery. I plotted individual traces (Figure 3.14) and mean trace with standard error of the mean (SEM) (Figure 3.13). This indicates the relative fluorescence in the cells relative to baseline, which shows the change in activity of the cell. In both males and females, I observe a fast rise in activity very shortly after the onset of the stimulus, which then decreases more slowly (Figure 3.13A-D). This is the case for both the first and second sucrose deliveries. Cells responding to lactic acid show an even more rapid increase, although it is slightly more delayed after the onset of the stimulus (Figure 3.13E-F). The subsequent decrease in activity of the cell is also more rapid compared to 55 the sucrose responding cells. Finally, I only observed cells responding to sodium chloride in females. The shape of these responses was slightly different. The rise in activity tended to be slower, followed by a second peak, before an eventual slow decrease (Figure 3.13G). 56 Figure 3.13 – Trace responses to tastants Mean trace responses with SEM to first 100 mM sucrose delivery in females (A) and males (B), second 100 mM sucrose delivery in females (C) and males (D), 100 mM lactic acid in females (E) and males (F), and 100 mM NaCl in females (G). Red bar indicates tastant delivery period. N is indicated on each graph. 57 Figure 3.14 – Individual trace responses to tastants Individual trace responses to first 100 mM sucrose delivery (A), 100 mM lactic acid (B), 100 mM NaCl (C), and the second 100 mM sucrose delivery (D). Female responses on the left, male responses on the right. Red bar indicates tastant delivery period. N is indicated on each graph. 58 I then organized the cells based on their response (Figure 3.15A-B). The peak F/F percent value of each cell at the onset of each tastant is represented in the heat map, with warmer colours indicating a higher peak value. The peak F/F was then plotted for each cell type in females (Figure 3.15C,E,G) and males (Figure 3.15D,F). Here the non-parametric Friedman test with Dunn’s multiple comparison test was used to compare each value with the peak F/F value of the tastant that each cell is responding to. In both females and males, sucrose-responding cells show a significant response to sucrose compared to lactic acid and sodium chloride, which they do not respond to (Figures 3.15C-D). Responses to sucrose in males seem to be of a larger amplitude than in females. In both females and males, lactic acid-responding cells show a large peak F/F value in response to lactic acid, however this is not significantly different from the response to sucrose and salt (Figures 3.15E-F). This may be due to the large variation across individuals and the extremely low sample size. Responses to lactic acid in males seem larger than in females. Finally, no male response to sodium chloride were recorded. Female sodium chloride-responding cells show a large peak F/F value in response to sodium chloride compared to sucrose and lactic acid, however this is only significantly different from the lactic acid response (Figure 3.15G). This may again be due to the high variation across individuals and the low sample size. Overall, we observe that different populations of cells respond to sucrose, lactic acid, and sodium chloride. We also observe that the responses to these tastants are similar in males and females, showing no sexual dimorphism. 59 Figure 3.15 – Separate cell populations respond to appetitive tastants (A-B) Heat map of peak F/F values of individual cells in response to 100mM sucrose, 100mM lactic acid, 100mM NaCl, and 100 mM sucrose in females (A) and males (B). Each row represents individual cells. (C-G) Bar graphs of peak F/F values of cells in response to 100mM sucrose in females (C) and males (D), 100mM lactic acid in females (E) and males (F), and 100mM NaCl in females (G). Bar represents mean response with SEM bars, individual data points indicated. Friedman test with Dunn’s multiple comparisons test ( = 0.05). N indicated on each graph. 60 Chapter 4: Discussion 4.1 Characterization of the anatomy of GRN populations in the tarsus of Aedes aegypti mosquitoes The tarsus is the first gustatory organ to make physical contact with a substrate when a mosquito lands, and is important in driving behaviours that are influenced by gustation (Owen, 1963). Different populations of GRNs in the tarsus express different receptors that are tuned to specific taste molecules (Baik & Carlson, 2020). Here, we aimed to characterize certain populations of neurons that express receptors important in gustation in mosquitoes. We first evaluate pan-neuronal expression in the tarsus, and then compare the more distinct populations of cells expressing Ir25a, Gr4, and ppk301. These comparisons were done between the three legs of both males and females. Our analysis of pan neuronal expression showed larger cell counts in females compared to males, as well as a higher distribution of cells in the foreleg of females compared to other legs (Figure 3.3A). Due to the different average sizes of the sexes and the many taste-driven sexually dimorphic behaviours, we may expect differences in neurons expressed in taste organs across the sexes. As seen in previous studies in various insect species, the foreleg is more heavily involved in the detection of tastants and tends to show higher expression of neurons (Konopka et al., 2023; Ling et al., 2014b; Morita et al., 2023). This may account for the higher cell count in females in the foreleg specifically, and is consistent with previous studies that looked only at the fifth, most distal tarsomere in Ae. aegypti (Morita et al., 2023) and Anopheles coluzzii (Konopka et al., 2023). The pan neuronal driver also shows increasing densities of cells towards the more distal tarsomeres in all legs of males and females (Figure 3.8). When a mosquito is resting on a 61 surface, the most distal tarsomeres tend to make to most physical contact with that surface (Figure 1.1A-C). Usually, the three most distal tarsomeres of the foreleg and midleg, and the two most distal of the hindleg will touch the surface that the mosquito is on. This may account for the higher density of sensory neurons in those segments. Our analysis of cells expressing Ir25a shows higher densities in the foreleg and midleg of females compared to males and demonstrates that both sexes contain the smallest population in the hindleg (Figure 3.3B). Some taste-driven behaviours, such as blood-feeding and egg-laying, are sexually dimorphic, being performed by females but not males (Christophers, 1960). The pattern of cell distribution that we observe across the legs is consistent with previous studies in Ae. aegypti (Morita et al., 2023). When the mosquito is on a substrate, the foreleg and midleg tend to make more physical contact with that substrate, so these legs may contain more cells important for gustation. In both sexes, we also observe larger densities of cells in the most distal tarsomeres (Figure 3.9). The more distal tarsomeres make more physical contact with a substrate when the mosquito is on it, which also means that they may be more involved in taste responses coming from the leg. Whether the mosquito is on the skin of a host, on a nectar source, or on water, the more proximal tarsomeres are less likely to physically touch the surface (Figure 1.1). Seeing as Ir25a is heavily involved in chemosensation, neurons expressing this receptor may be denser in the tarsomeres that can detect non-volatile gustatory molecules. Perhaps the more proximal tarsomeres are more heavily involved in the detection of volatile molecules. In the Gr4-expressing cells, there are no significant differences in the number of cells between males and females in any of the legs, but both sexes show sparser populations of cells in the hindleg compared to other legs (Figure 3.3C). Gr4 is a receptor that is thought to respond to sugars and cells expressing it are involved in driving nectar feeding behaviour (Jové et al., 2020). 62 Seeing as nectar feeding is conserved across both sexes, it might be expected that expression is similar across males and females. The distribution pattern of the cells across the legs is consistent with previous studies in Ae. aegypti females (Morita et al., 2023). When nectar feeding, the hindleg tends to hang in the air, making no physical contact with the surface (Figure 1.1B). This could explain the smaller density of neurons involved in nectar feeding behaviour in the hindleg. Our analysis of ppk301-expressing cells shows no significant difference in populations between the two sexes, or across the legs (Figure 3.3D). Although ppk301 was shown to be involved in egg-laying behaviour (Matthews et al., 2019), which is only performed by females, males may also utilize a fresh-water receptor to seek water sources and prevent desiccation. This could explain the highly similar cell counts between the two sexes. Mosquitoes on water seem to make contact with the water surface with all three legs (Figure 1.1C). This could account for the similar distribution of ppk301-expressing cells across the legs. When resting on the water surface (Figure 1.1C), and perhaps touching borders right above the waterline, the mosquito uses more surface of all three legs. This may indicate a need for freshwater sensors distributed throughout the whole tarsus, as is observed in our results (Figure 3.11). The brp cell population shows the greatest variation in the number of neurons across different individuals (Figure 3.2). However, we observe an increasing amount of variation as cell count increases across all drivers, but the proportion of variability (SEM) relative to the mean is the same. Therefore, we have no evidence that variability relative to the mean is higher in brp cells compared to other drivers. The variation in cell count could be due to biological differences or technical issues. Technical errors could arise for larger cell populations as counting becomes slightly more difficult. Dense clusters of neurons can introduce uncertainty in differentiating cell bodies from one another. The expression of the fluorophore also seemed less uniform in the brp 63 driver, which could be due to certain cell types expressing it differently. Biological differences in number of neurons across different individuals is also possible. Biological variation is often present across individuals to allow flexibility in responding to natural selection (Bijma, 2011). Variation amongst individuals was also observed in previous studies of neuroanatomy in mosquitoes (Konopka et al., 2023; Morita et al., 2023). High variability amongst individuals in a population can allow for a larger range of responses, which can be beneficial for adaptability should the need arise. 4.2 Functional imaging protocol In this study, we analyze changes in cell activity during stimulus delivery via calcium imaging. Calcium imaging techniques restrict the species that can be studied as we need the genetic tools to introduce transgenes into them (Wei et al., 2020). It also restricts which populations of cells we can analyzed based on the driver/reporter constructs we have available. Despite this, calcium imaging is preferable to electrophysiology for analysing responses of individual cells from a specific genetically-defined population. Using this technique, we may also sample the neural activity of multiple neurons in one plane of view (Wei et al., 2020). We developed a novel functional imaging protocol that allowed us to acquire GCaMP signals from individual cells while delivering liquid taste stimuli to the legs. The troubleshooting process informed aspects that were necessary for the protocol to work. This allowed us to investigate whether the same population of cells responds to appetitive tastants in the mosquito tarsus. 64 4.2.1 Troubleshooting the functional imaging protocol Although many small changes were made throughout the troubleshooting process, two large ones will be discussed here: the change from an upright to an inverted microscope and the use of a yellow mutant mosquito rather than a wildtype one. When using an upright microscope, the immersion objective is dipped into the solution that is being delivered to the sample. Water that was purified using a Millipore MilliQ system was used for the baseline solution that the sample was kept in. When a new solution replaced the MilliQ water, there was a change in the osmolarity, and a small amount of turbulence. This often caused an obstruction between the objective and the sample, resulting in an unreliable GCaMP signal. The use of an inverted microscope, in which the objective is oriented below the sample, solved this obstacle. In wildtype Ae. aegypti mosquitoes, the scales on the legs are abundant and quite dark in colour (Super, 1962), which can obstruct the view of the neurons. In order to counteract this, we removed the scales from the tarsi by gently brushing with a paint brush. No reliable responses were obtained when this was performed, which led us to believe that the scale removal process might also be damaging tarsal sensilla. This would prevent the physical contact of the dendrites with taste molecules. For this reason, we started using transgenic mosquitoes in a yellow mutant background. These mutants contain a lighter cuticle, which eliminates the need for scale removal. These changes enabled the acquisition of reliable signals in cells as a response to given stimuli. 65 4.2.2 Non-overlapping cells respond to attractive tastants in the mosquito tarsus Previous studies in D. melanogaster have determined that subpopulations of sweet GRNs are also involved in the response to non-sugar appetitive tastants. Some subpopulations of neurons responding to sugars also responds to lactic acid (Stanley et al., 2021), and to sodium chloride at a low threshold of activation (Dweck et al., 2022). In mosquitoes, previous studies have determined that neurons in the mouthparts of the mosquito are distinct for responses to sugars and blood cues (Jové et al., 2020). Different mouthparts are utilized for nectar and blood feeding, and these contain only neurons responding to their respective appetitive cue (Jové et al., 2020). Here, we aim to answer whether neurons at the level of the tarsus, which detects appetitive tastants before the mouthparts, respond to these different appetitive cues in overlapping or distinct populations of neurons. We investigate whether lactic acid and sodium chloride, both components found in sweat and blood (Ament et al., 1997; Klous et al., 2021; McSwiney, 1934), would elicit a response in cells that also respond to sucrose, a component of nectar (Chalcoff et al., 2006). We carried out this analysis in both male and female mosquitoes. We hypothesized that overlapping populations of cells would respond to the different appetitive tastants, and also that males would not respond to the blood-feeding cues. Our results reject both hypotheses. Our analysis shows that neurons in the tarsus of mosquitoes that respond to sucrose, do not in fact respond to lactic acid or sodium chloride (Figure 3.15). Fewer cell responses were acquired for lactic acid and sodium chloride, and they were distinct from sucrose-responding cells. Our analysis also indicates that there are no differences between males and females. Although no responses to sodium chloride were 66 observed in males, some responses to the other blood-feeding cue lactic acid, were detected (Figures 3.13-15). Similarly to females, these were in distinct populations from sucrose-responding cells. The distinction in mosquitoes from D. melanogaster could elucidate the different behaviours of the two species. The appetitive cues that drive feeding in D. melanogaster elicit a general feeding behaviour, whereas in mosquitoes, there are two distinct forms of feeding. Nectar and blood feeding in mosquitoes are detected and consumed via different mechanisms, but they also serve different functions. A female mosquito will only need to blood-feed in order to produce eggs, whereas both male and female mosquitoes will feed on nectar for survival (Christophers, 1960). The distinction between these two feeding mechanisms in mosquitoes could account for the need to distinguish their appropriate cues at the level of the taste organs. This perhaps explains the distinct cells populations needed for appetitive cues that drive blood feeding or nectar feeding in mosquitoes. The presence of cells responding to the blood-feeding cue lactic acid in male mosquitoes could be attributed to their attraction to blood hosts. Although males do not blood feed, they have been known to hover around humans to facilitate mating with females (Hartberg, 1971). 4.3 Future directions 4.3.1 For anatomy These results increase our knowledge on the expression of receptors in the tarsus. This could be expended upon by characterizing the anatomy of various other populations of neurons. Other receptors known to have a role in gustation could be of interest here. These could be informed by orthologs in D. melanogaster that carry out known functions. 67 There are also other aspects of neuronal anatomy that may be interesting to explore. For instance, the projection patterns of the neuronal populations, and which sensilla they innervate. The number and placement of sensilla on the tarsi of Ae. aegypti have been previously described (Super, 1962). Some recent imaging studies have looked at the innervation patterns of specific populations of neurons in the fifth tarsomere of female Ae. aegypti (Morita et al., 2023). The description of neuronal projections and innervation patterns across the more proximal portions of the tarsus, as well as in males remains elusive. These may be interesting to describe clustering patterns in neuronal populations and infer population-specific functions. We know that females contain almost twice the amount of sensilla as males (Super, 1962), but this ratio does not hold for the amount of neurons. Further exploration into the co-expression of various important taste receptors may also be of interest. This includes co-expression of dense co-receptors, such as Ir25a and Ir76b, and the co-expression of widely expressed co-receptors and other tastant-specific receptors. Similar studies have been performed in the past, although many characterizations of overlap remain understudied in mosquito gustation. For instance, one study used two different driver/reporter construct within the same strain to characterize the overlap of Ir25a and Ir76b in D. melanogaster tarsi (Y. Chen & Amrein, 2017). Another study utilized a split driver/reporter construct, which only drives expression of the effector in cells with targeted co-expression, as well as immunostaining and in situ hybridization to confirm protein co-expression (Herre et al., 2022). They characterized the overlap between the widely expressed olfactory receptor co-receptor (orco) and three major ionotropic co-receptors, Ir25a, Ir76b, and Ir8a in the antennae and maxillary palp of Ae. aegypti (Herre et al., 2022). 68 4.3.2 For functional imaging setup Having obtained reliable responses to liquid taste stimuli in a mosquito sensory organ opens up many doors for future studies. The sucrose cell experiment presented in this thesis could be replicated to obtain a larger sample size. One potential option for this would be to use a cell population known to respond to sucrose, to eliminate the proportion of runs that show no response. In this case, we could target Gr4-expressing cells, as they are thought to respond to sucrose (Jové et al., 2020), and evaluate whether these cells responding to sucrose also respond to appetitive blood-feeding cues. Other appetitive blood-feeding cues such as ATP or sodium bicarbonate could also be added to the panel of stimuli. Another interesting experiment may be to investigate bitter-responding cells. As we know from D. melanogaster, bitter GRNs may express multiple different bitter receptors, which respond to different bitter compounds. However, not all bitter GRNs express all bitter receptors (Kaushik & Kain, 2020). Overlap in the response to different bitter compounds may be of interest in mosquitoes as well. Many bitter and other unpalatable compounds underly the aversiveness of insect repellents. Therefore, understanding the underlying cellular basis of the aversion to different compounds will help us understanding these coding strategies. Another interesting experiment could attempt to understand the cellular basis of salt taste coding. The mechanisms underlying salt taste coding are more complex than that of other tastants. Low concentrations of salt are appetitive and elicit positive feeding responses, whereas high concentrations of salt are aversive and elicit avoidance behaviour (Taruno & Gordon, 2023). In D. melanogaster, studies have shown that specific receptors are involved in low salt attraction and high salt aversion, but that all classes of GRNs respond to salt in some way (Dweck et al., 69 2022; McDowell et al., 2022; Taruno & Gordon, 2023). In Ae. aegypti, ppk301-expressing GRNs are known to respond to high and low concentrations of salt (Matthews et al., 2019). Using a pan-neuronal driver to explore responses to salt in a concentration-dependent manner may be of interest in mosquitoes. For instance, investigating whether some cells responding to high aversive concentrations of salt may be separate to cells responding to low appetitive concentrations. The possibilities for these experiments are endless! 70 Bibliography Acree, F., Turner, R. B., Gouck, H. K., Beroza, M., & Smith, N. (1968). L-Lactic Acid: A Mosquito Attractant Isolated from Humans. Science, 161(3848), 1346–1347. https://doi.org/10.1126/science.161.3848.1346 Afify, A., & Galizia, C. G. (2015). Chemosensory Cues for Mosquito Oviposition Site Selection. Journal of Medical Entomology, 52(2), 120–130. https://doi.org/10.1093/jme/tju024 Ahn, J.-E., Chen, Y., & Amrein, H. (2017). Molecular basis of fatty acid taste in Drosophila. eLife, 6, e30115. https://doi.org/10.7554/eLife.30115 Ament, W., Huizenga, J., Mook, G., Gips, C., & Verkerke, C. J. (1997). Lactate and Ammonia Concentration in Blood and Sweat During Incremental Cycle Ergometer Exercise. International Journal of Sports Medicine, 18(01), 35–39. https://doi.org/10.1055/s-2007-972592 Baik, L. S., & Carlson, J. R. (2020). The mosquito taste system and disease control. Proceedings of the National Academy of Sciences, 117(52), 32848–32856. https://doi.org/10.1073/pnas.2013076117 Behura, S. K., Haugen, M., Flannery, E., Sarro, J., Tessier, C. R., Severson, D. W., & Duman-Scheel, M. (2011). Comparative Genomic Analysis of Drosophila melanogaster and Vector Mosquito Developmental Genes. PLoS ONE, 6(7), e21504. https://doi.org/10.1371/journal.pone.0021504 Benton, R., Vannice, K. S., Gomez-Diaz, C., & Vosshall, L. B. (2009). Variant Ionotropic Glutamate Receptors as Chemosensory Receptors in Drosophila. Cell, 136(1), 149–162. https://doi.org/10.1016/j.cell.2008.12.001 71 Bijma, P. (2011). A General Definition of the Heritable Variation That Determines the Potential of a Population to Respond to Selection. Genetics, 189(4), 1347–1359. https://doi.org/10.1534/genetics.111.130617 Cameron, P., Hiroi, M., Ngai, J., & Scott, K. (2010). The molecular basis for water taste in Drosophila. Nature, 465(7294), 91–95. https://doi.org/10.1038/nature09011 Carter, M., Essner, R., Goldstein, N., & Iyer, M. (2022). Visualizing and Measuring Neural Dynamics. In Guide to Research Techniques in Neuroscience (pp. 169–189). Elsevier. https://doi.org/10.1016/B978-0-12-818646-6.00005-1 Chalcoff, V. R., Aizen, M. A., & Galetto, L. (2006). Nectar Concentration and Composition of 26 Species from the Temperate Forest of South America. Annals of Botany, 97(3), 413–421. https://doi.org/10.1093/aob/mcj043 Chen, T.-W., Wardill, T. J., Sun, Y., Pulver, S. R., Renninger, S. L., Baohan, A., Schreiter, E. R., Kerr, R. A., Orger, M. B., Jayaraman, V., Looger, L. L., Svoboda, K., & Kim, D. S. (2013). Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature, 499(7458), 295–300. https://doi.org/10.1038/nature12354 Chen, Y., & Amrein, H. (2017). Ionotropic Receptors Mediate Drosophila Oviposition Preference through Sour Gustatory Receptor Neurons. Current Biology, 27(18), 2741-2750.e4. https://doi.org/10.1016/j.cub.2017.08.003 Christophers, S. R. (1960). Aedes aegypti (L.) the yellow fever mosquito: Its life history, bionomics and structure. Cambridge University Press. Clyne, P., Warr, C., & Carlson, J. R. (2000). Candidate Taste Receptors in Drosophila. Science, 287(5459), 1830–1834. https://doi.org/10.1126/science.287.5459.1830 72 Croset, V., Rytz, R., Cummins, S. F., Budd, A., Brawand, D., Kaessmann, H., Gibson, T. J., & Benton, R. (2010). Ancient Protostome Origin of Chemosensory Ionotropic Glutamate Receptors and the Evolution of Insect Taste and Olfaction. PLoS Genetics, 6(8), e1001064. https://doi.org/10.1371/journal.pgen.1001064 Dahanukar, A., Lei, Y.-T., Kwon, J. Y., & Carlson, J. R. (2007). Two Gr Genes Underlie Sugar Reception in Drosophila. Neuron, 56(3), 503–516. https://doi.org/10.1016/j.neuron.2007.10.024 Day, J. (2016). Mosquito Oviposition Behavior and Vector Control. Insects, 7(4), 65. https://doi.org/10.3390/insects7040065 Delventhal, R., Kiely, A., & Carlson, J. R. (2014). Electrophysiological Recording From Drosophila Labellar Taste Sensilla. Journal of Visualized Experiments, 84, 51355. https://doi.org/10.3791/51355 Dennis, E. J., Goldman, O. V., & Vosshall, L. B. (2019). Aedes aegypti Mosquitoes Use Their Legs to Sense DEET on Contact. Current Biology, 29(9), 1551-1556.e5. https://doi.org/10.1016/j.cub.2019.04.004 Dweck, H. K. M., Talross, G. J. S., Luo, Y., Ebrahim, S. A. M., & Carlson, J. R. (2022). Ir56b is an atypical ionotropic receptor that underlies appetitive salt response in Drosophila. Current Biology, 32(8), 1776-1787.e4. https://doi.org/10.1016/j.cub.2022.02.063 Fölsz, O., Lin, C.-C., Task, D., & Riabinina, O. (2022). The Q-system: A Versatile Repressible Binary Expression System. Methods in Molecular Biology, 2540, 35–78. https://doi.org/10.1007/978-1-0716-2541-5_2 73 Freeman, E. G., & Dahanukar, A. (2015). Molecular neurobiology of Drosophila taste. Current Opinion in Neurobiology, 34, 140–148. https://doi.org/10.1016/j.conb.2015.06.001 Fujii, S., Yavuz, A., Slone, J., Jagge, C., Song, X., & Amrein, H. (2015). Drosophila Sugar Receptors in Sweet Taste Perception, Olfaction, and Internal Nutrient Sensing. Current Biology, 25(5), 621–627. https://doi.org/10.1016/j.cub.2014.12.058 Gordon, R. M., & Lumsden, W. H. R. (1939). A Study of the Behaviour of the Mouth-Parts of Mosquitoes when Taking Up Blood from Living Tissue; Together with Some Observations on the Ingestion of Microfilariae. Annals of Tropical Medicine & Parasitology, 33(3–4), 259–278. https://doi.org/10.1080/00034983.1939.11685071 Grant, A. J., Aghajanian, J. G., O’Connell, R. J., & Wigton, B. E. (1995). Electrophysiological responses of receptor neurons in mosquito maxillary palp sensilla to carbon dioxide. Journal of Comparative Physiology A, 177(4). https://doi.org/10.1007/BF00187475 Hartberg, W. K. (1971). Observations on the Mating Behaviour of Aedes aegypti in Nature. Bulletin of the World Health Organization, 45(6), 847–850. Herre, M., Goldman, O. V., Lu, T.-C., Caballero-Vidal, G., Qi, Y., Gilbert, Z. N., Gong, Z., Morita, T., Rahiel, S., Ghaninia, M., Ignell, R., Matthews, B. J., Li, H., Vosshall, L. B., & Younger, M. A. (2022). Non-canonical odor coding in the mosquito. Cell, 185(17), 3104-3123.e28. https://doi.org/10.1016/j.cell.2022.07.024 Jaeger, A. H., Stanley, M., Weiss, Z. F., Musso, P.-Y., Chan, R. C., Zhang, H., Feldman-Kiss, D., & Gordon, M. D. (2018). A complex peripheral code for salt taste in Drosophila. eLife, 7, e37167. https://doi.org/10.7554/eLife.37167 74 Jiao, Y., Moon, S. J., & Montell, C. (2007). A Drosophila gustatory receptor required for the responses to sucrose, glucose, and maltose identified by mRNA tagging. Proceedings of the National Academy of Sciences, 104(35), 14110–14115. https://doi.org/10.1073/pnas.0702421104 Jones, W. D., Cayirlioglu, P., Grunwald Kadow, I., & Vosshall, L. B. (2007). Two chemosensory receptors together mediate carbon dioxide detection in Drosophila. Nature, 445(7123), 86–90. https://doi.org/10.1038/nature05466 Jové, V., Gong, Z., Hol, F. J. H., Zhao, Z., Sorrells, T. R., Carroll, T. S., Prakash, M., McBride, C. S., & Vosshall, L. B. (2020). Sensory Discrimination of Blood and Floral Nectar by Aedes aegypti Mosquitoes. Neuron, 108(6), 1163-1180.e12. https://doi.org/10.1016/j.neuron.2020.09.019 Kaushik, S., & Kain, P. (2020). Understanding Taste Using Drosophila melanogaster. In E. Tvrdá & S. Chandra Yenisetti (Eds.), Animal Models in Medicine and Biology. IntechOpen. https://doi.org/10.5772/intechopen.89643 Kent, L. B., & Robertson, H. M. (2009). Evolution of the sugar receptors in insects. BMC Evolutionary Biology, 9(1), 41. https://doi.org/10.1186/1471-2148-9-41 Kim, S. H., Lee, Y., Akitake, B., Woodward, O. M., Guggino, W. B., & Montell, C. (2010). Drosophila TRPA1 channel mediates chemical avoidance in gustatory receptor neurons. Proceedings of the National Academy of Sciences, 107(18), 8440–8445. https://doi.org/10.1073/pnas.1001425107 Klous, L., De Ruiter, C. J., Scherrer, S., Gerrett, N., & Daanen, H. A. M. (2021). The (in)dependency of blood and sweat sodium, chloride, potassium, ammonia, lactate and 75 glucose concentrations during submaximal exercise. European Journal of Applied Physiology, 121(3), 803–816. https://doi.org/10.1007/s00421-020-04562-8 Kong, X. Q., & Wu, C. W. (2010). Mosquito proboscis: An elegant biomicroelectromechanical system. Physical Review E, 82(1), 011910. https://doi.org/10.1103/PhysRevE.82.011910 Konopka, J. K., Task, D., Poinapen, D., & Potter, C. J. (2023). Neurogenetic identification of mosquito sensory neurons. iScience, 26(5), 106690. https://doi.org/10.1016/j.isci.2023.106690 Kumar, A., Tauxe, G. M., Perry, S., Scott, C. A., Dahanukar, A., & Ray, A. (2020). Contributions of the Conserved Insect Carbon Dioxide Receptor Subunits to Odor Detection. Cell Reports, 31(2), 107510. https://doi.org/10.1016/j.celrep.2020.03.074 Kwon, J. Y., Dahanukar, A., Weiss, L. A., & Carlson, J. R. (2007). The molecular basis of CO2 reception in Drosophila. Proceedings of the National Academy of Sciences, 104(9), 3574–3578. https://doi.org/10.1073/pnas.0700079104 Latorre-Estivalis, J. M., Almeida, F. C., Pontes, G., Dopazo, H., Barrozo, R. B., & Lorenzo, M. G. (2021). Evolution of the Insect PPK Gene Family. Genome Biology and Evolution, 13(9), evab185. https://doi.org/10.1093/gbe/evab185 Li, M., Bui, M., Yang, T., Bowman, C. S., White, B. J., & Akbari, O. S. (2017). Germline Cas9 expression yields highly efficient genome engineering in a major worldwide disease vector, Aedes aegypti. Proceedings of the National Academy of Sciences, 114(49). https://doi.org/10.1073/pnas.1711538114 76 Ling, F., Dahanukar, A., Weiss, L. A., Kwon, J. Y., & Carlson, J. R. (2014a). The Molecular and Cellular Basis of Taste Coding in the Legs of Drosophila. The Journal of Neuroscience, 34(21), 7148–7164. https://doi.org/10.1523/JNEUROSCI.0649-14.2014 Ling, F., Dahanukar, A., Weiss, L. A., Kwon, J. Y., & Carlson, J. R. (2014b). The Molecular and Cellular Basis of Taste Coding in the Legs of Drosophila. The Journal of Neuroscience, 34(21), 7148–7164. https://doi.org/10.1523/JNEUROSCI.0649-14.2014 Liu, M. Z., & Vosshall, L. B. (2019). General Visual and Contingent Thermal Cues Interact to Elicit Attraction in Female Aedes aegypti Mosquitoes. Current Biology, 29(13), 2250-2257.e4. https://doi.org/10.1016/j.cub.2019.06.001 Matthews, B. J., Dudchenko, O., Kingan, S. B., Koren, S., Antoshechkin, I., Crawford, J. E., Glassford, W. J., Herre, M., Redmond, S. N., Rose, N. H., Weedall, G. D., Wu, Y., Batra, S. S., Brito-Sierra, C. A., Buckingham, S. D., Campbell, C. L., Chan, S., Cox, E., Evans, B. R., … Vosshall, L. B. (2018). Improved reference genome of Aedes aegypti informs arbovirus vector control. Nature, 563(7732), 501–507. https://doi.org/10.1038/s41586-018-0692-z Matthews, B. J., McBride, C. S., DeGennaro, M., Despo, O., & Vosshall, L. B. (2016). The neurotranscriptome of the Aedes aegypti mosquito. BMC Genomics, 17(1), 32. https://doi.org/10.1186/s12864-015-2239-0 Matthews, B. J., Younger, M. A., & Vosshall, L. B. (2019). The ion channel ppk301 controls freshwater egg-laying in the mosquito Aedes aegypti. eLife, 8, e43963. https://doi.org/10.7554/eLife.43963 77 McDowell, S. A. T., Stanley, M., & Gordon, M. D. (2022). A molecular mechanism for high salt taste in Drosophila. Current Biology, 32(14), 3070-3081.e5. https://doi.org/10.1016/j.cub.2022.06.012 McIver, S., & Siemicki, R. (1978). Fine structure of tarsal sensilla ofAedes aegypti (L.) (Diptera: Culicidae). Journal of Morphology, 155(2), 137–155. https://doi.org/10.1002/jmor.1051550202 McMeniman, C. J., Corfas, R. A., Matthews, B. J., Ritchie, S. A., & Vosshall, L. B. (2014). Multimodal Integration of Carbon Dioxide and Other Sensory Cues Drives Mosquito Attraction to Humans. Cell, 156(5), 1060–1071. https://doi.org/10.1016/j.cell.2013.12.044 McSwiney, B. A. (1934). The Composition of Human Perspiration (Samuel Hyde Memorial Lecture): Samuel Hyde Memorial Lecture. Proceedings of the Royal Society of Medicine, 27(7), 839–848. https://doi.org/10.1177/003591573402700710 Menda, G., Nitzany, E. I., Shamble, P. S., Wells, A., Harrington, L. C., Miles, R. N., & Hoy, R. R. (2019). The Long and Short of Hearing in the Mosquito Aedes aegypti. Current Biology, 29(4), 709-714.e4. https://doi.org/10.1016/j.cub.2019.01.026 Miyamoto, T., Slone, J., Song, X., & Amrein, H. (2012). A Fructose Receptor Functions as a Nutrient Sensor in the Drosophila Brain. Cell, 151(5), 1113–1125. https://doi.org/10.1016/j.cell.2012.10.024 Montell, C. (2021). Drosophila sensory receptors—A set of molecular Swiss Army Knives. Genetics, 217(1), 1–34. https://doi.org/10.1093/genetics/iyaa011 78 Morita, T., Lyn, N. G., Von Heynitz, R. K., Goldman, O. V., Sorrells, T. R., DeGennaro, M., Matthews, B. J., Houri-Zeevi, L., & Vosshall, L. B. (2023). Cross-modal sensory compensation increases mosquito attraction to humans [Preprint]. Neuroscience. https://doi.org/10.1101/2023.10.10.561721 Ni, L. (2021). The Structure and Function of Ionotropic Receptors in Drosophila. Frontiers in Molecular Neuroscience, 13, 638839. https://doi.org/10.3389/fnmol.2020.638839 Owen, W. B. (1963). The contact chemoreceptor organs of the mosquito and their function in feeding behaviour. Journal of Insect Physiology, 9(1), 73–87. https://doi.org/10.1016/0022-1910(63)90085-4 Riabinina, O., Luginbuhl, D., Marr, E., Liu, S., Wu, M. N., Luo, L., & Potter, C. J. (2015). Improved and expanded Q-system reagents for genetic manipulations. Nature Methods, 12(3), 219–222. https://doi.org/10.1038/nmeth.3250 Riabinina, O., & Potter, C. J. (2016). The Q-System: A Versatile Expression System for Drosophila. In C. Dahmann (Ed.), Drosophila (Vol. 1478, pp. 53–78). Springer New York. https://doi.org/10.1007/978-1-4939-6371-3_3 Robertson, H. M., & Kent, L. B. (2009). Evolution of the Gene Lineage Encoding the Carbon Dioxide Receptor in Insects. Journal of Insect Science, 9(19), 1–14. https://doi.org/10.1673/031.009.1901 Salgado, V. L. (2017). Insect TRP channels as targets for insecticides and repellents. Journal of Pesticide Science, 42(1), 1–6. https://doi.org/10.1584/jpestics.D16-104 79 Sanford, J. L., Shields, V. D. C., & Dickens, J. C. (2013). Gustatory receptor neuron responds to DEET and other insect repellents in the yellow-fever mosquito, Aedes aegypti. Naturwissenschaften, 100(3), 269–273. https://doi.org/10.1007/s00114-013-1021-x Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. https://doi.org/10.1038/nmeth.2019 Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 Singh, R. N. (1997). Neurobiology of the gustatory systems ofDrosophila and some terrestrial insects. Microscopy Research and Technique, 39(6), 547–563. https://doi.org/10.1002/(SICI)1097-0029(19971215)39:6<547::AID-JEMT7>3.0.CO;2-A Sorrells, T. R., Pandey, A., Rosas-Villegas, A., & Vosshall, L. B. (2022). A persistent behavioral state enables sustained predation of humans by mosquitoes. eLife, 11, e76663. https://doi.org/10.7554/eLife.76663 Stanley, M., Ghosh, B., Weiss, Z. F., Christiaanse, J., & Gordon, M. D. (2021). Mechanisms of lactic acid gustatory attraction in Drosophila. Current Biology, 31(16), 3525-3537.e6. https://doi.org/10.1016/j.cub.2021.06.005 Super, E. H. (1962). Sensory Hairs with Permeable Tips on the Tarsi of the Yellow-Fever Mosquito, Aedes aegypti. Annals of the Entomological Society of America, 55(5), 531–535. https://doi.org/10.1093/aesa/55.5.531 80 Taruno, A., & Gordon, M. D. (2023). Molecular and Cellular Mechanisms of Salt Taste. Annual Review of Physiology, 85(1), 25–45. https://doi.org/10.1146/annurev-physiol-031522-075853 Wei, Z., Lin, B.-J., Chen, T.-W., Daie, K., Svoboda, K., & Druckmann, S. (2020). A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology. PLOS Computational Biology, 16(9), e1008198. https://doi.org/10.1371/journal.pcbi.1008198 Wint, W., Jones, P., Kraemer, M., Alexander, N., & Schaffner, F. (2022). Past, present and future distribution of the yellow fever mosquito Aedes aegypti: The European paradox. Science of The Total Environment, 847, 157566. https://doi.org/10.1016/j.scitotenv.2022.157566 Wolff, D. (2006). Nectar Sugar Composition and Volumes of 47 Species of Gentianales from a Southern Ecuadorian Montane Forest. Annals of Botany, 97(5), 767–777. https://doi.org/10.1093/aob/mcl033 Yee, D. A., Dean Bermond, C., Reyes-Torres, L. J., Fijman, N. S., Scavo, N. A., Nelsen, J., & Yee, S. H. (2022). Robust network stability of mosquitoes and human pathogens of medical importance. Parasites & Vectors, 15(1), 216. https://doi.org/10.1186/s13071-022-05333-4 Zhao, Z., Tian, D., & McBride, C. S. (2021). Development of a pan-neuronal genetic driver in Aedes aegypti mosquitoes. Cell Reports Methods, 1(3), 100042. https://doi.org/10.1016/j.crmeth.2021.100042 81 Zwiebel, L. J., & Takken, W. (2004). Olfactory regulation of mosquito–host interactions. Insect Biochemistry and Molecular Biology, 34(7), 645–652. https://doi.org/10.1016/j.ibmb.2004.03.017
Thesis/Dissertation
eng
Zoology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Matthews, Benjamin J., 1983-
Gordon, Michael Douglas
The neural basis of taste coding in Aedes aegypti mosquitoes
Text