Mass spectrometric quantification of arousal associated

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Mass spectrometric quantification of arousal associated neurochemical changes in single honey bee brains and brain regions Divya Ramesh, and Axel Brockmann ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00254 • Publication Date (Web): 16 Oct 2018 Downloaded from http://pubs.acs.org on October 19, 2018

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ACS Chemical Neuroscience

Mass spectrometric quantification of arousal associated neurochemical changes in single honey bee brains and brain regions Divya Ramesh* and Axel Brockmann National Centre for Biological Sciences Apis mellifera, mass spectrometry, biogenic amines, behavior, central nervous system, quantification

ABSTRACT: Honey bee foragers show a strong diurnal rhythm of foraging activity and such behavioral changes are likely under the control of specific neuromodulators. To identify and quantify neuromodulators involved in regulating rest and arousal in honey bees, we established a mass spectrometric method for quantifying 14 different neurochemicals and precursor molecules. We measured forager type and brain region specific differences in amine levels from individual honey bee brains and brain regions. The observed differences in amine levels between resting and aroused foragers resemble findings in other species indicating a conserved molecular mechanism by glutamate and GABA in regulating arousal. Subesophageal ganglion specific changes in the histaminergic system and global increases in aspartate during arousal suggest a possible role of histamine and aspartate in feeding and arousal, respectively. More aminergic systems were significantly affected due to arousal in nectar foragers than in pollen foragers, implying that forager phenotypes differ not only in their food preference, but also in their neuromodulatory signaling systems (brain states). Finally, we found that neurotransmitter precursors were better at distinguishing brain states in the central brain, while their end products correlated with arousal associated changes in sensory regions like the optic and antennal lobes.

The Western honey bee (Apis mellifera) is one of the major animal models to study social behavior and the evolution of sociality1–3. A hallmark of honey bee social organization is the age-related division of labor and behavioral specialization of genetically very similar individuals. Analyses of the molecular mechanisms underlying the regulation of social behaviors have focused on neuromodulation by biogenic amines like octopamine, dopamine, and serotonin. There is strong experimental evidence that neuromodulator systems are involved in behavioral maturation1, scouting behavior4, defense5 and any kind of task specialization6. The task specialization in the social honey bee is based on behavioral maturation which implies that worker bees of a specific age show only a subset of all possible behaviors a worker could perform. Further, foragers can be distinguished into distinct behavioral groups of scouts, recruits, pollen and nectar foragers. These behaviorally specialized bees are assumed to represent different behavioral states 7. Differences in the brain neuromodulator profile are generally associated with behavioral state changes, which in turn are generated by distinct brain states. Brain states, e.g. nurse or a forager brain, differ in their gene expression profiles8 which in turn can include changes in neurotransmitter and neuromodulators signaling systems. Besides behavioral specialization, sleep and wakefulness present a daily occurring change in brain state. Sleep and wakefulness are accompanied by large changes in firing frequencies of neurons, regulated by long range neuromodulatory inputs9– 11. Broad changes in firing activity could also be involved and be characteristic for task specialization, particularly if

one assumes stimulus response thresholds as major factors regulating of division of labor in insects. There are reports that the circadian clock controls many aspects of the brain including the neuron cell size12, regulation of neuromodulation13, as well as learning and memory14. We are interested in the question whether daily foraging activity involves the activation of specific neuromodulator systems. Honey bee foragers show a strong diurnal activity rhythm15,16. Under restricted feeding conditions, when food is available only during a few hours of the day, they anticipate the availability of food and prepare themselves by gathering at the entrance of the hive before the expected feeder time17,18. This kind of anticipation can be viewed as a behavioral state change, much like the change from rest to arousal in other animals.

Figure 1. Choice of internal standard is important. a) TyramineD4 does not undergo matrix effects. b) Serotonin-D4 undergoes strong ion suppression due to the matrix effect.

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Table 1. Summary of differences in the methods used by Nivedita et al.a and this work Sample processing detail

Nivedita et al.

This work

Sonication of samples

Water bath sonicator

Bead beating

Calibration curve ranges

0.14 ng/mL to 20 ng/mL for all

Target amount-based ranges

ISTD concentrations

20 ng for all compounds

Lower 1/3rd of calibration curve ranges for high abundance amines

Matrix effect

Not calculated

Tested (reported in Table 2)

Standard addition

Not done

Performed for 5-HT and Tryptamine

Derivatizing agent

1 mg/mL AQC

10 mg/mL AQC

Reconstituting agent

0.5% Acetonitrile

2% Acetonitrile (0.5% FA)

Reconstitution volume

50 µl

50 µl for whole, OL, CB and SEG, 25 µl for AL

a19

Studies on fruit flies have identified neuromodulators like octopamine and dopamine as key players in arousal and wakefulness13,20–23. However, studies correlating neuromodulator levels with rest and arousal are few, to the best of our knowledge. Our work seeks to bridge this gap using a mass spectrometric method that is sensitive enough to quantify 14 different analytes even from brain regions of individual honey bees. Traditionally, quantification of biogenic amines from small tissue samples of non-genetic insect model organisms has been done using HPLC coupled to electrochemical detectors (ECD)5,24,25. Although this is a highly sensitive technique, it has a few drawbacks, the most important being the inability to use isotope labeled internal standards (ISTDs) to account for a matrix effect (ME: the effect in which the same concentration of an analyte gives different instrument responses, due to the presence of interfering substances in complex biological samples)26. This is because the principle on which ECD functions is based on the oxido-reduction of compounds, and therefore, isotope labelled internal standards respond exactly like the analyte, which makes it impossible to distinguish between the two. Secondly, the identification of compounds from a sample is done by extrapolating the response of the standard in a neat solvent. A complex biological sample like the brain will have many interfering compounds that can co-elute with each other resulting in overestimation of concentrations. This loss in specificity can be overcome by improved separation techniques. The problem regarding overestimation can be solved by separating the sample into multiple aliquots, spiking some of them with known concentration of standards and calculating the difference (standard addition). Both strategies would necessitate longer processing periods, thus reducing the throughput. Finally, other electroanalytical methods like for e.g. fast scan cyclic voltammetry (see ref. 27), only detects changes and not absolute levels of analytes in the tissue. More importantly, all these techniques, as well as microdialysis, require that the animal is restrained, and/or that a probe is placed with high spatial precision. This makes these techniques impractical in situations when studying genetically nontractable animals in the wild.

Figure 2. a) Comparison of brain volume, cell density and aminergic cell numbers. b) Relative distribution of amine levels. Only neurotransmitter precursors show similar distribution to cell density and brain volume. CB, central brain; OL, optic lobe; SEG, sub-esophageal ganglion; Al antennal lobe. For this analysis, only morning caught bees with measurements available for all brain regions were used. This includes 18 pollen and 10 nectar foragers. The same data was used to generate the correlation plot in Fig. S1. a28, b 29, c 30, d 31, e 32, f 33, g34, h35, i36


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Figure 3. Boxplots of neurochemicals showing amine level changes associated with arousal in the AL. The ‘#’ is indicative of ratios of analytes that compete for the same neurotransmitter precursor. Data from 53 bees (17 nectar: resting =7, aroused =10; 36 pollen: resting =18, aroused =18). Resting and aroused indicate bees caught from within the hive at 02:00 a.m. and 08:00 a.m., respectively. Bees were trained to forage at either a scented nectar feeder or a pollen feeder at 09:00 a.m.

Many of these limitations of electroanalytical methods can be overcome by using mass spectrometry (MS). By using tandem MS, ions can be fragmented, and their daughter ions analyzed. The procedure provides high specificity, as even m/z identical compounds will have a unique fragmentation pattern. This allows for the quantification of a large set of compounds simultaneously without the complications related to the aspect of separation. Moreover, this high specificity means that ISTDs can be used and matrix effects can be accounted for in the quantification of analytes in complex biological samples. Qualitative mass spectrometric studies on spatial distribution of biogenic amines in animal tissue have been done using desorption electrospray Ionization MS (DESI), secondary ion MS37 or quantitatively using MALDI-MS imaging38. LC-ESI-MS/MS was used in the quantification of a few biogenic amines in pooled samples of 10 fly heads26. In recent years, mass spectrometry has also been successfully used in insect neuropeptidomic studies. MS has been used not only for profiling the neuropeptide inventory of the bee brain39, but also for honey bee neuropeptide quantification (LC-ESI-MS/MS) and distribution (MALDI-TOF MS)40,41. The ionization technique: Electrospray ionization (ESI), Desorption ESI, Matrix-assisted laser desorption/ionization (MALDI) etc., and mass analyzers: the Orbitraps, quadrupole, Time-of-flight (TOF) etc., are selected depending on the specific goal of the project. Each technique has its own advantages and disadvantages. Furthermore, small molecule analysis of complex biological matrices can either be done using expensive very high-resolution Fourier transform-ion cyclotron-resonance mass spectrometers, or they can be subject to prior chromatographic separation like gas or liquid chromatography, and capillary

electrophoresis (CE). CE-ESI-MS is one of the most sensitive ways quantify metabolites from small volume samples, even up to the level of a single cell42. However, it has problems of robustness when compared to LC and GC separations for complex biological samples43 and it is not the most widespread platform in metabolomics analysis. In this work, we established a highly sensitive LC-ESIMS/MS method to measure modulators from single brains and brain regions of the honey bee, Apis mellifera, and we quantified changes associated with rest and arousal. Results and Discussion Although quantification of biogenic amines using mass spectrometry has become widespread, establishing such a method to quantify numerous compounds from minute sample amounts has not been done so far. In this work, we successfully established an MS based method to provide a comprehensive view on neuromodulators in individual insect brain regions. We studied the honey bee, since it displays a large repertoire of social behaviors, and we focused on changes associated with rest and arousal. Method development and validation We were initially started quantifying neurochemicals in single brains using a method successfully developed for whole body measurements in planaria19. However, we found that the sample amount of bee brains insufficient for quantification. Therefore, we adjusted and optimized some key elements to improve quantification of smaller amounts of biological samples. Table 1 lists the changes applied in this study. Our study was designed as a targeted one and therefore we used selected reaction monitoring (SRM), as is used in many



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Table 2. Robustness of method as measured by regression lines, coefficient of variation (CV%) and matrix effect (ME%) Target range 5-200%

Target range 0.5-40%

Compound

Equation

R2

Histidine

y= 2.021 x + 0.911

Serine

CV% of LLOQ

ME%

Equation

R2

1.000 6.389

40.00

y= 2.39 x + 0.661

0.999 21.519

27.65

y= 1.702 x + 0.494

0.999 11.239

90.95

y= 1.724 x + 1.545

0.999 22.987

100.17

Histamine

y= 1.591 x + 0.028

1.000 4.836

29.13

y= 1.783 x + 0.008

0.999 24.573

11.23

Aspartate

y= 0.866 x + 22.715

0.997 3.014

52.13

y= 0.7 x + 1.159

0.994 23.166

57.96

Glutamate

y= 1.125 x + 23.613

0.997 1.903

64.57

y= 1.115 x + 0.687

0.999 8.432

79.45

GABA

y= 0.914 x + 6.578

0.998 2.450

59.99

y= 1.149 x + 0.34

0.995 23.307

65.22

DOPA

y= 1.884 x + 0.01

0.997 11.822

72.20

y= 1.775 x + 0.003

0.997 20.133

113.67

Octopamine

y= 6.685 x + 0.123

0.985 15.491

= DOPA y= 9.917 x - 0.017

0.985 16.974

= DOPA

Tyrosine

y= 1.697 x + 0.084

1.000 3.364

75.21

y= 1.727 x + 0.265

0.998 9.369

111

Dopamine

y= 1.433 x + 0.005

1.000 8.658

83.83

y= 1.387 x + 0.001

0.999 10.205

117.41

Serotonin

y= 0.871 x + 0.008

0.995 0.000

32.77

y= 1.115 x + 0.007

0.997 9.428

16.81

Tyramine

y= 6.622 x - 0.065

0.996 127.357

= 5-HT

y= 6.338 x - 0.002

1.000 52.022

= 5-HT

Tryptophan

y= 1.537 x + 1.273

0.996 4.869

301.86

y= 2.34 x + 1.773

0.995 3.361

230.72

Tryptamine

y= 1.159 x + 0.003

0.998 21.651

801.43

y= 0.957 x + 0.005

0.983 4.562

402.95

routine analyses44. We performed several methodological steps to increase our confidence in the identities of the analytes. We performed AQC derivatization, followed by a sample cleanup using a C-18 column. Furthermore, synthetic standards as well as isotope labeled internal standards were also used. Finally, we performed a LC separation which allowed us to identify the retention times of each of the analytes of interest based on these standards and ISTDs (details of above methods and control experiments given in Supporting Information Figure S4 and table S5). We improved the sensitivity of our method using SRM. In case the identities of the analytes need to be confirmed, it is possible to shift to MRM. As a proxy for repeatability and the robustness of the method, the values of calibration curves run across all batches of samples were analyzed. The values of the best fit regression line, the coefficient of regression, the percent coefficient of variation (CV%) for the Lower Limit Of Quantification (LLOQ), as well as the percent matrix effect (ME%) are given in Table 2. The ME% was estimated using ISTD peak area differences between the calibration curves and the samples and employing the equation: (peak area in matrix / peak area in neat solvent) *100. For the method of standard addition, we did the following. We spiked a known amount of a mixture of 5-HT and tryptamine to each of the samples, as well as to a vial of the neat solvent. We subtracted the neat solvent response from the sample response and estimated the sample concentration from the calibration curve for 5-HT and tryptamine. Regression coefficients for analytes in both concentration ranges were greater than 0.98. The CV% of the lowest calibration point in both ranges showed good repeatability over the duration of the study, with values falling below 25%. For Tyramine (TA) and Octopamine (OA), no

CV% of LLOQ

ME%

deuterated forms were available, and therefore, serotoninD4 and DOPA-D3, respectively, were used. The high CV% for TA indicated that serotonin-D4 was not a suitable standard. This was because serotonin underwent massive matrix ion suppression while TA did not (Fig. 1). In contrast, we found that DOPA-D3 was an appropriate ISTD for OA. We were able to procure isotope labeled OA and TA, the ideal internal standards, only towards the end stages of the study. However, because the other estimates of robustness and repeatability were good, we decided to continue using serotonin-D4 and DOPA-D3 as internal standards for the later analyses. Part-wise distribution of biogenic amines in the honey bee brain Our measurements of biogenic amines levels in A. mellifera brains were within the range of previously reported levels5,6,45–50 (Table S1, Supporting Information). The spatial distribution of amino acid and neurotransmitter precursor levels (Histidine, DOPA, Tyrosine, Glutamate etc.) strongly correlated with both neuronal cell density as well as brain volume, as calculated from published literature28,29 (Fig. 2 and Fig. S1, Supporting Information). However, classical biogenic amines failed to follow this pattern. With the exception of dopamine (DA), the spatial distribution of all major biogenic amine levels failed to reflect the distribution of the location of their aminergic cell bodies. We also found that octopamine and serotonin (5-HT) levels did not reflect the spatial distribution of their receptors. Though the distribution of aminergic cells, their processes as well as their binding sites have been well studied30–34,36,51–53, studies comparing the levels of these amines with their somatic distribution, or even receptor binding sites had been lacking. Our results indicated that these do not match. A similar mismatch in Apis mellifera had been previously


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Figure 4. Boxplots of neurochemicals showing amine level changes associated with arousal in the CB. The ‘#’ is indicative of ratios of analytes that compete for the same neurotransmitter precursor. Data from 53 bees (17 nectar: resting =7, aroused =10; 36 pollen: resting =18, aroused =18). Resting and aroused indicate bees caught from within the hive at 02:00 a.m. and 08:00 a.m., respectively. Bees were trained to forage at either a scented nectar feeder or a pollen feeder at 09:00 a.m.

reported for amines in relation to their binding sites and the distribution of immunoreactivity. This had generally been explained by the phenomenon of “volume transmission”, where non-synaptic release and long-range diffusion of neuromodulators takes place51,54. The lack of correlation between distributions of cells, receptors, and the amine content stresses the importance of such measurements in the understanding of neuromodulation. Tryptamine levels were significantly negatively correlated (r = -0.6, p < 0.0001) with both cell numbers and density (Fig. S1, Supporting Information). This indicated that bees, and maybe other insects as well, have a pathway to convert tryptophan to tryptamine, and that this pathway is differentially active in the different brain regions. Tryptamine has not been reported to be an endogenous neuromodulator in insects55. Rest and arousal associated changes in brain neurochemistry Based on our analyses, we grouped our results into two types of changes – brain region specific or forager type specific changes and global changes. We report changes in relation to the aroused phenotype for each forager type separately. Therefore, ‘higher’ and ‘lower’ indicate higher and lower levels in the morning (aroused) as compared to night (resting), respectively. Brain region specific or forager type specific changes The first category of changes analyzed were those that were seen only in specific brain regions or were dependent

on the forager type. We present the results below moving from sensory regions to the central brain. Antennal Lobes (olfactory system) In the Antennal Lobes (AL), we found common modulatory systems being affected in divergent ways in the two forager types. Levels of OA and DA were higher in the morning in pollen foragers, while OA, DA, and TA (mean change = -0.16 ng, SE = ± 0.076 ng) were lower in nectar foragers (Fig. 3). In addition, GABA (mean change = +19.9 ng, SE = ± 5.6 ng) and glutamate (mean change = +58.8 ng, SE = ± 28.2 ng) levels were higher only in pollen foragers. Our results suggest antennal lobe processing changes in a forager specific manner. Glutamate and GABA are components of the inhibitory networks in honey bee antennal lobe neuron cultures56. Studies have reported that they are both involved in different forms of inhibitory olfactory processing in the ALs of Drosophila57. In addition, pollen foragers are more sensitive to sucrose than nectar foragers58 and OA is reported to increase responsiveness to antennal chemosensory cues59. Our results showing higher neuromodulator levels in aroused pollen foragers could, therefore, indicate that the AL becomes primed for heightened sensitivity prior to anticipated foraging activity. It is intriguing that foragers that were trained to food sources, both being odorous, responded in such opposing ways in their primary olfactory centers. Optic Lobes (visual system) In the Optic Lobes (OL), the ratio of GABA to glutamate was reduced in both kinds of foragers in the morning (mean change = -0.02, SE = ±0.006) (Fig. S2, Supporting Information). This reduction was independent of individual



GABA and glutamate levels, indicating that the regulation was at the level of relative amounts. Candidate neurons in the OL that could be capable of modulating the glutamate to GABA ratios are the GABAergic neurons. These are known to be responsive to light60 and also mediate entrainment61. TA, the precursor to OA, increased (mean change = +0.25 ng, SE = ±0.11 ng), while tryptophan, the precursor to 5-HT, decreased (mean change = -2.25 ng, SE = ±0.70 ng) during arousal in nectar foragers. Previous studies on visual system processing showed that OA and 5-HT had antagonistic functions in modulating motion sensitivity, with the former increasing direction sensitivity and the latter decreasing it34. In addition, we found OA levels to be higher in pollen foragers than in nectar foragers reflecting a previously published result62.

Figure 5. Linear discriminant analysis (LDA) of central brain amines shows that neurotransmitter precursors predict brain states better than products. The blue and red colors indicate the resting and aroused brain states respectively. Aspartate was excluded for this analysis. The abbreviations used to describe each sample are as follows- 2: Resting (02:00 a.m. caught), 8: Aroused (08:00 a.m. caught); N: Nectar, P: Pollen; 15: colony identity. a) LDA using precursors of neuromodulators showed clear separation of brain states. The amines used in this analysis were Histidine, Serine, Glutamate, Tyrosine, Tryptophan, Tyramine, and DOPA. b) LDA using neuromodulators alone did not show separation of brain states. The amines used for this analysis are Histamine, GABA, Tryptamine, Dopamine, Serotonin, and Octopamine.

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Central brain (CB) region (mushroom bodies, central complex, lateral protocerebrum) We found the starkest difference between the two forager types in this brain region. In the levels of analytes between the resting and aroused states, we found nine different changes in the nectar foragers and a single change in the pollen foragers (Fig. 4). Of these, the most interesting changes were the higher OA/TA ratio (mean change = +0.16, SE = ±0.04) in the aroused nectar foragers, as well as the lower dopaminergic precursor levels. Tyrosine (mean change = -8.53ng, SE = ±3.6ng) and L-DOPA (mean change = 0.12ng, SE = ±0.04) levels as well as the DOPA to tyrosine ratio (mean change = -0.0026, SE = ±0.001) were lower in the morning. These results suggested that there is a possibility of increased metabolism of precursors to catecholamines before activity. OA is known to be the arousal neuromodulator50 and both OA and DA have been implicated as strong wake promoting neuromodulators in flies20. A linear discriminant analysis showed that neurotransmitter precursors (His, Ser, Glu, Tyr, Trp, DOPA, TA), as indicated by their location in neuromodulator synthesis pathways, were better at separating brain states than their product biogenic amines (Fig. 5). Aspartate was excluded because of its singularly large effect in classifying brain states. This region-specific pattern reveals an important significance of precursors in regulating biogenic amine levels, as the latter do not always correlate with behavioral state26,63. This finding is supported, first, by studies where the biogenic amine levels as well as the behavioral state is changed by feeding precursors 64. Second, it is known that expression and activity of the amino acid decarboxylases and hydroxylases are regulated with behavioral state65–6865–68. Third, a recent study in mice showed that tryptophan and not 5-HT levels are correlated with the transition from sleep to wakefulness69. Sub-esophageal ganglion (SEG) Our experiments showed higher histidine levels in both forager types in the SEG (pollen: mean change = +4.5ng, SE = ± 2.2ng; nectar: mean change = +12.8ng, SE = ± 4.7ng). Nectar foragers showed higher HA levels (mean change = +0.2ng, SE = ± 0.071ng) as well. DA levels were lower (mean change = -67pg, SE = ±16pg) in the SEG of nectar foragers during arousal (Fig. S3, Supporting Information). The SEG is a less studied region of the honey bee brain and is involved in controlling the mouthparts, and relaying and processing of sensory information from them70–72. Currently, the only functionally identified neuron from the honey bee SEG is the octopaminergic ventral unpaired medial (VUM) mx1 neuron which relays the unconditional stimulus and causes a proboscis extension reflex (PER)73. It is still unknown which of the other SEG neuron(s) are responsible for modulating sucrose sensitivity71. Recent immunocytochemistry studies in honey bees have identified two new dopaminergic neurons in the SEG that have been suggested to correspond to dopaminergic VUM neurons in fruit flies53. One of these, the tyrosine hydroxylase-VUM neuron was shown to modulate sucrose sensitivity and PER in the fruit fly74. Injection of DA into honey bee thorax reduces their sucrose responsiveness75.


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Our results, therefore, are suggest a possible SEG dependent modulation of sucrose sensitivity prior to foraging activity. Such a modulation would be unnecessary for pollen foragers since they collect pollen in their corbiculae, unlike the nectar foragers who collect sucrose solution using their proboscis and know the reward instantaneously76. Histamine is an anorectic modulator of food intake in vertebrates77 and it regulates aspects of feeding behavior in Aplysia78. Our results, therefore, indicate a largely unexplored role of the histaminergic system in feeding and arousal related behaviors in honey bees. Global changes Two amino acids that showed global changes across all regions of the brain in the same way were aspartate and tryptophan. As the feeding time approached, levels of aspartate rose while levels of tryptophan fell across all brain regions tested (Table S2, Supporting Information). In addition, the ratio between GABA and its precursor, glutamate, were lower across all brain regions during arousal. Previous reports suggest that the glutamate and GABAergic systems are a conserved molecular mechanism governing novelty-seeking behavior during foraging4. Reports on vertebrates showed that arousal states are correlated with increased glutamate and decreased GABA levels in the cortex79, supporting our results. Table 3. Target and ISTD amounts of different analytes on column Analyte

Target amount on column (ng)

ISTD amount on column (ng)

DOPA

0.05

2.86

Serotonin

0.125

2.86

Tryptamine

0.125

2.86

Tyramine

0.4

2.86

Dopamine

1

2.86

Octopamine

1

2.86

Histamine

4

2.86

Tryptophan

5

2.86

Tyrosine

20

2.86

Histidine

25

2.5

Serine

40

2.5

GABA

100

20

Aspartate

625

50

Glutamate

625

50

Do specific parts change in relation to the whole brain? Apart from comparing differences in amine levels in the brain, we also analyzed the change in their proportion within the four brain regions to gain a holistic understanding of modulatory processes. During this analysis, certain changes that did not show up previously, specifically, the source of modification of the dopaminergic system in the antennal lobes were revealed (Table S2,

Supporting Information). The change in the proportion of tyrosine in the AL correlated with the change in absolute amounts of DA and OA. This is suggestive of an active regulation of DA and OA synthesis caused by modifying the availability of the precursor. We also found that the number of level variations seen in the CB of nectar foragers were reduced and maximum changes in proportions of analytes were seen in the AL. This could indicate that perhaps foraging related sensory modalities are the first to be affected during the anticipation of foraging. In our study, we found a greater number of arousal associated changes in nectar foragers than in pollen foragers. We found most differences in the absolute amounts of neuromodulators in the CB of nectar foragers than in any other part of the brain. However, the most changes in proportions were seen in the AL. Together, these results seem to point towards a divide in the modulatory systems mediating arousal in the two forager types. A previous study on honey bee division of labor found correlations between brain amines and mRNAs expression levels of the corresponding enzymes68. Our results showing changes in the central brain dopaminergic system is strengthened by studies on food anticipation and time training, in which bees anticipating the feeder showed upregulation of dopamine receptor genes in the mushroom bodies80,81. The sensitivity of our method opens the possibility of combining brain region-wise comparisons of neurotransmitter levels and corresponding gene expression changes. Conclusions We have established a mass spectrometric method for detecting and quantifying 14 different analytes from low sample amounts such as individual honey bee brains and brain regions. Our study characterizes neurochemical signatures of honey bee brain states of rest and arousal. We found support for a conserved molecular mechanism for glutamate and GABA in mediating arousal. SEG specific changes in the histaminergic system and a global increase in aspartate during arousal seem to suggest a yet unexplored role for these amines in feeding and arousal, respectively. In sensory regions like the optic and antennal lobes, arousal associated changes were correlated with changes in biogenic amine levels. Interestingly, in the CB, the rest and arousal brain states were better correlated with neurotransmitter precursor levels than with their products. In addition, we found forager phenotype (nectar and pollen) associated changes as well. We found that the number of changes seen in nectar foragers is more, even though the core behavior in both forager types is similar, viz. anticipation of foraging. This finding suggested that not only do forager types differ in their food preference, but also in their brain states. The improvements made in this method were successfully adapted with minimum changes to measure OA from 5 pooled Drosophila heads63. Methods Chemicals and reagents All standards, ammonium acetate, formic acid (FA), hydrochloric acid (HCl), boric acid and ascorbic acid as well as reagents required for 6-aminoquinolyl-N-



hydroxysuccinimidyl carbamate (AQC) synthesis were obtained from Sigma-Aldrich (Bangalore, India). Acetone was obtained from Fisher Scientific. Reverse-Phased Solid Phase Extraction (RP-SPE) cartridges (Strata-X, 8B-S100TAK) were obtained from Phenomenex, Inc. (Hyderabad, India). High purity MS grade solvents (methanol, acetonitrile, and water) were obtained from Merck Millipore (Merck Millipore India Pvt. Ltd., Bangalore). Deuterated internal standards19 were supplied by CDN isotope (Quebec, Canada). Calibration curves and standard addition The analytes were weighed individually and made into standard stocks in 0.1 N HCl. Target amounts for the different analytes were calculated from preliminary experiments. Calibration curves were made by diluting the standard stocks to 200%, 160%, 80%, 40%, 20%, 10% and 5% of the target amounts for the larger brain regions. Calibration curves for the AL ranged from 40%, 20%, 10%, 5% and 0.5% of target amounts. Individual ISTDs were also made to a stock solution of 1 mg/mL in 0.1 N HCl and then mixed to fall within the lower one-third of the calibration curves for highly abundant analytes. The final amounts on the column for the ISTDs as well as the target amounts for the different analytes are given in Table 3. For the AL, all ISTDs were added to achieve an amount of 0.5 ng on the column. AQC was synthesized according to Nivedita et al.19. AQC working solutions of 10 mg/mL were freshly prepared on the days of sample processing. Calibrants were made by adding the appropriate standard and ISTD amounts to 190 µl of acetone in 0.1% FA and 10 µl of freshly made 10 mM ascorbic acid and lyophilized (ScanVac). These were processed in the same way as the brain samples described below. Separate mixtures of serotonin and tryptamine were made to 0.5 µg/mL in 0.1N HCl to measure these trace analytes using the standard addition method. Different brain regions required slightly different added concentrations (whole brains, 0.45 ng; OL, 0.2 ng; all other parts, 0.5 ng). Animals and behavioral experiments Apis mellifera colonies were acquired from a local beekeeper and maintained in an enclosed flight chamber within the grounds of the research institute. Bees were provided with a 2 M sucrose solution feeder containing 0.1% linalool (Sigma) or a pollen (Hi-tech Natural Products (India) Ltd) feeder. For the experiments, colonies were trained to collect either sucrose solution or pollen, for two hours starting at 09:00 a.m. (local time), for 10 days. The foragers at the feeder were marked on the thorax with paint marks (Posca Paint Pens, Mitsubishi Pencil Co., Japan) on day 7. Marked bees were collected from within the colony at 02:00 a.m. and 08:00 a.m. (local time) on day 10 and immediately flash-frozen in liquid nitrogen and stored at 80 oC till processing. No bees had begun flying at 08:00 a.m. Experiments were repeated for pollen foragers thrice, and for nectar foragers twice, from a total of 5 different colonies. 100 pollen foragers and 40 nectar foragers were caught.

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Total numbers used for analyses are mentioned in the next section. Sample processing and MS analysis Bees were decapitated, and the brains were removed from the head capsule over dry ice to prevent amine degradation82. The pigmented retina of the complex eyes and the ocelli, as well as attached hypopharyngeal glands and trachea were removed. For individual brain region measurements, a subgroup of brains was further divided into pairs of optic lobes (OL), antennal lobes (AL), subesophageal ganglion (SEG) and the remaining region, the central brain (CB). The division was done along the natural fracture lines between the neuropils. The 2 optic lobes, 2 antennal lobes, sub-esophageal ganglion, and the central brain from each bee was collected separately in labelled microcentrifuge vials. There was a total of 25 resting pollen, 24 aroused pollen and 10 nectar foragers per time point for whole brains. For OL, a total of 61 pairs (42 pairs pollen, 21 rest, 21 aroused; and 19 pairs nectar, 9 rest, 10 aroused) were used. For CB, AL and SEG, a total of 53 bees were analyzed (36 pollen, 18 rest, 18 aroused; 17 nectar, 7 rest, 10 aroused). The latter are also the number of bees with all brain regions analyzed. We tested the effect of a 12 h incubation at room temperature and at 37 oC, in light and dark conditions (Supporting Information Fig. S7). In our experiment, we found that for neat solvent, incubation at 37 oC incubation had the most negative effect in amine quantification. Light had a negative effect only on histamine and tyramine detection. There are no effects of temperature on the AQC reacted amines83. In all our sample preparations, samples were stored in the dark till the time of processing and ISTDs were added to the samples before moving them to room temperature. Thus, any effect of temperature and light will affect our samples and controls equally. The use of isotope labelled internal standards also counteracted any effect of solvent on amine detection. Acetone (190 µl in 0.1% FA), 10 µl of freshly made 10 mM ascorbic acid and the appropriate amount of ISTD and standards were added to the vials containing the brains/ brain regions. 100 µl of 0.5 mm glass beads (BioSpec) were added and the tissues were homogenized in a bead beater (BioSpec) for 1 min. The vials were then centrifuged at 13,500 rpm at 4 oC for 10 min and the supernatant was collected in a new vial. These were then dried in a cooling vacuum centrifuge (ScanVac). The derivatization protocol used was modified from Nivedita et.al19. Briefly, 10 mg/mL AQC was made in 100% ACN and added to the reconstituted buffered sample and allowed to react at 55 oC for 10 min. The reaction was stopped using 3 µl of 100% FA. MS grade water (500 µl) was then added to the samples and loaded onto activated and equilibrated RP-SPE columns. Elution was done with 1 mL of ACN-MeOH (4:1) in 0.1% FA. Samples were reconstituted in 2% ACN in 0.5% FA. Reconstitution volume was adjusted for antennal lobes separately to increase detection. Ten microliters of each reconstituted sample were injected for LC-MS/SRM analysis.


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We used a Thermo Scientific TSQ Vantage triple stage quadrupole mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA), connected to an Agilent 1290 infinity series UHPLC system (Agilent Technologies India Pvt. Ltd., India). The column oven was set at 40 oC, and the autosampler tray at 4 oC. The mobile phase solvent A was 10 mM ammonium acetate containing 0.1% FA, and the mobile phase solvent B was ACN containing 0.1% FA. LC and MS operating conditions were as described in Nivedita et al.19. We used a C-18 column (2.1 x 100 mm, 1.8 μm, Agilent RRHD ZORBAX) fitted with a guard column (2.1 x 5 mm, 1.8 μm Agilent ZORBAX SB-C18). The MS operating conditions were: 3700 V: spray voltage (positive ion mode); 270 oC: capillary temperature; 20: sheath gas pressure; 10: auxiliary gas (arbitrary units); argon: collision gas. The S lens voltage, collision energy, parent and product ion m/z and retention time for each amine are given in Table S5, Supporting Information. Method validation Inter-day variation and dynamic range of calibration curves were calculated using runs from different batches. On days with large numbers of samples (>30), calibration curves were run in between the samples to check for drift in responses. Matrix effects were calculated as a percentage using ISTD peak area differences between the calibration curves and the samples and employing the equation: (peak area in matrix / peak area in neat solvent) *100. Recovery was calculated as the percentage of accuracy of estimating correctly the intermediate target concentrations (10, 40 and 160%; Table S6, Supporting Information). Chromatograms of the highest calibration point as well as an example specimen, showing separation between mass identical amines OA and DA, are given in Fig. S4 a and b (Supporting Information). The calibration curves are given in Fig. S5 (Supporting Information). Fig. S6 shows the difference in analyte peak areas in whole brain samples treated with 10 mg/mL AQC and 1 mg/mL AQC. Area ratios of standards to ISTD, as generated by the quantification software (Xcalibur, version 2.2 SP1.48), were used to generate the best fit line with 1/x weighting. The lowest calibration point was chosen only when signal-to-noise ratio >10 units, as a proxy for the Lower Limit of Quantitation (LLOQ). Only during detection with fluorescence or UV is there interference from AMQ (6-aminoquinoline), the AQC hydrolysis product. However, this was previously shown as a non-issue when a UPLC-MS/MS method is used 84. Ion stability, limits of detection and structure were not revalidated as they were the same as in 19.ISTDs for two amines (octopamine and tyramine) were not available, therefore DOPA-D3 and serotonin-D4 were used as their ISTDs, respectively19. Regression analysis was done using R (v3.4.0) statistical software85. Data analysis Data analysis was done using linear mixed effects models in R (v3.4.0)85 with the lmer function from the lme4 package. Models were made to test for effects of time of capture on individual amine levels, for each forager type and brain

region separately. The colony and MS batch were specified as random effects in each model. Ratios between absolute levels were also tested for significance in case they were part of the same biosynthetic pathway, or if they were competitive for the same precursor. The significance of the effect was tested using Satterthwaite approximations in the lmerTest package. Linear Discriminant Analysis (LDA) was performed on the same transformed data using the “MASS” package in R, with “leave-one-out cross-validation” to estimate the classification accuracy of the models. Correlation plots were generated using the “corrplot” package in R, with significance level cutoff at 0.0001 (details in Supporting Information).

ASSOCIATED CONTENT Supporting Information Previously reported values of biogenic amines levels in Apis mellifera, correlogram of quantified analytes with published literature reports on spatial distribution of biogenic amines, boxplots showing amine levels in OL and SEG, chromatograms and calibration curves, validation of derivatization results of LMM analyses, number of samples analyzed, MS settings and source data for literature reports. This material is available free of charge via the Internet at http://pubs.acs.org. The raw data are available at Mendeley Data with the doi:10.17632/gy99bm269w.1 (post publication)

AUTHOR INFORMATION Corresponding Author *[email protected]

Present Addresses National Centre for Biological Sciences, Bellary Road, Bangalore, India.

Author Contributions DR and AB designed the study. DR performed and analyzed the experiments, DR and AB wrote the manuscript.

Funding Sources NCBS institutional fund to AB (12P4167), CSIR SPM fellowship to DR

ACKNOWLEDGMENT The authors thank Dr. Kannan Rangiah, Nivedita Natarajan and Dr. Padma Ramakrishnan from the NCBS/C-CAMP mass spectrometry facility for their help in access, usage, and troubleshooting of the TSQ Vantage Instrument. We also wish to thank Dr. Sebastian Sturm for comments on the earlier versions of the manuscript. We would like to thank Dr. Sruthi Unnikrishnan for help with statistical analyses. This study was funded by the NCBS institutional fund to AB (12P4167) and CSIR-SPM fellowship to DR.

ABBREVIATIONS FA, Formic Acid; HA, Histamine; His, Histidine; Ser, Serine; Asp, Aspartate; Glu, Glutamate; GABA, γ-Aminobutyric acid; DOPA, Dihydroxyphenylalanine; DA, Dopamine; Tyr, Tyrosine; Trp, Tryptophan; 5-HT, 5-Hydroxytryptamine; OA, Octopamine; TA, Tyramine; ACN, Acetonitrile; MeOH, Methanol; ISTD, Internal Standard; AL, Antennal Lobe; OL, Optic Lobe; SEG, Subesophageal Ganglion; CB, Central Brain



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Mass spectrometric quantification of arousal associated

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