Correlations between foraging intensity and structural characteristics of the mushroom bodies.

<p>Individual values (n = 13) for the parameters of the lip <b>(A, B, C)</b> and dense collar <b>(D, E, F)</b> are plotted against foraging intensity: neuropilar volume <b>(A, D)</b>, density of synaptic boutons <b>(B, E),</b> total number of synaptic boutons <b>(C, F)</b>. The volume of the lip and collar, as well as the total number of boutons per lip, correlate positively with foraging intensity (Spearman rank correlations).</p

Modelled consequences of varying MB connectivity on reversal learning performance.

<p>Modelled percentage of individuals displaying PER in response to odours A (<i>red line</i>) and B (<i>orange line</i>) during the reversal learning paradigm. Three different models were run simulating a sparse <b>(A)</b> or dense <b>(B)</b> distribution of excitatory connections onto MB neurons (KC), and <b>(C)</b> sparse with suppressed inhibitory input from the GABAergic PCT. 200 agents (virtual bees) were modelled for each model configuration. The 95% confidence intervals are represented by the black lines.</p

Change in reversal learning performance with duration of foraging.

<p>Percentages of individuals displaying PER in response to odours A (<i>red line</i>) and B (<i>orange line</i>) are shown, during the first phase (A+B-) and the reversal phase (A-B+) of the reversal learning task. Results are presented for bees with increasing foraging durations defined by the 1^st quartile (Q₁ = 113.8min), median (Q₂ = 381.3min), and 3^rd quartile (Q₃ = 653.5min) of the total amount of time foraging of the whole sample. The bootstrapped 95% confidence intervals are indicated by the black lines. [<b>(A):</b> n = 21, <b>(B):</b> n = 21, <b>(C):</b> n = 20, <b>(D)</b>: n = 21] *** p < 0.0001, Tukey HSD <i>post hoc</i> analysis.</p

Mushroom body structure of orientating bees and foragers.

<p><b>(A)</b> Frontal confocal image of the right median MB labelled for synapsin (scale bar = 100μm). Borders of the lip (<i>orange</i>) and dense collar (<i>blue</i>) are highlighted. Boxplots showing the characteristics of the dense collar (<i>blue</i>) and lip (<i>orange</i>) of a sample of orientating bees (<i>O</i>, n = 5) and foragers (<i>F</i>, n = 13): <b>(B)</b> neuropil volume, <b>(C)</b> density of synaptic boutons, <b>(D)</b> number of synaptic boutons per neuropil. * p < 0.05, Mann-Whitney U-Test.</p

Reversal learning performance of precocious and normal-age foragers with short or long foraging durations.

<p>The proportions of non-learners (<i>NL</i>: <i>light grey</i>) and learners (<i>L</i>: <i>dark grey</i>) in the last two trials of the reversal phase (trials 4 and 5) are displayed. For each trial, bees were defined as non-learners or learners according to the value of their individual inversion score (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196749#sec002" target="_blank">Methods</a>; NL: IS = -1 or 0; L: IS = 1). The IS were compared between precocious and normal-age foragers, with either short or long foraging durations corresponding respectively to durations within or greater than the 1^st quartile of the whole sample (113.8min). [<i>Precocious</i>: short: n = 10, long: n = 39; <i>Normal-age</i>: short: n = 11, long: n = 23] * p < 0.01; ** p < 0.005, Mann-Whitney U-test.</p

Reversal learning performances of orientating bees and foragers with short or long foraging durations.

<p>The proportions of non-learners (<i>NL</i>: <i>light grey</i>) and learners (<i>L</i>: <i>dark grey</i>) in the last two trials of the reversal phase (trial 4 and 5) are displayed. For each trial, bees were defined as learners or non-learners according to the value of their individual inversion score (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196749#sec002" target="_blank">Methods</a>; NL: IS = -1 or 0; L: IS = 1). The IS are compared between orientating bees and foragers, with either short or long foraging durations corresponding respectively to durations within or outside the 1^st quartile of the whole sample (113.8min). [<i>Orientating</i>: n = 11; <i>Foragers-Short</i>: n = 21; <i>Foragers-Long</i>: n = 62] * p < 0.05; ** p < 0.01; *** p < 0.0005, Mann-Whitney U-Test.</p

Synaptic bouton density and number and reversal learning performance.

<p>Boxplots showing the characteristics of the dense collar (<i>blue</i>) and lip (<i>orange</i>) of non-learners (<i>NL</i>, IS = -1 or 0) and learners (<i>L</i>, IS = 1) for each of the last two trials of the reversal phase: <b>(A)</b> density of synaptic boutons, <b>(B)</b> number of synaptic boutons per neuropil. [<i>Trial 4</i>: n = 12 NL and 6 L; <i>Trial 5</i>: n = 10 NL and 8 L] * p < 0.05, ** p < 0.005, Mann-Whitney U-Test.</p

Characterization of functional broad-host range replicons in the honey bee gut symbiont <i>B</i>. <i>apis</i>.

(a) Broad-host range plasmids have different copy numbers in B. apis. Box plots show median values of plasmid copy numbers obtained by qPCR from 3 independent experiments with 5 biological replicate each (total n = 15). Median copy number are indicated with the corresponding box plots. (b) The difference in plasmid copy number results in different protein expression levels in B. apis. Graph shows mean of E2-crimson fluorescence ± standard deviations of 5 biological replicates. Each replicate represents the average fluorescence of at least 9,000 cells measured by flow cytometry. Plasmids used for panels a and b in B. apis were pBTK570, pAC06, pAC11, and pAC04, carrying the RSF1010, RK2, pTF-FC2, and pBBR1 origins of replication, respectively. (c) Some replicons are compatible and can be cotransformed in B. apis. Matrix table indicates compatible (green boxes with check mark) and incompatible (red boxes with cross mark) replicons. Vectors were found compatible upon their successful cotransformation by conjugation in B. apis cells. The data underlying this Figure can be found in the S1 Data file, sheets “Supplementary Fig 4A” and “Supplementary Fig 4B.” (PDF)</p

Fiji macro.

The honey bee is a powerful model system to probe host–gut microbiota interactions, and an important pollinator species for natural ecosystems and for agriculture. While bacterial biosensors can provide critical insight into the complex interplay occurring between a host and its associated microbiota, the lack of methods to noninvasively sample the gut content, and the limited genetic tools to engineer symbionts, have so far hindered their development in honey bees. Here, we built a versatile molecular tool kit to genetically modify symbionts and reported for the first time in the honey bee a technique to sample their feces. We reprogrammed the native bee gut bacterium Snodgrassella alvi as a biosensor for IPTG, with engineered cells that stably colonize the gut of honey bees and report exposure to the molecules in a dose-dependent manner through the expression of a fluorescent protein. We showed that fluorescence readout can be measured in the gut tissues or noninvasively in the feces. These tools and techniques will enable rapid building of engineered bacteria to answer fundamental questions in host–gut microbiota research.</div

Cell gating for flow cytometry analysis.

Graphs show pseudocolor plots used for gating of S. alvi cells. Quadrant limits were determined based on the measured fluorescence of reference cells bearing (a) the empty backbone pAC07 (no fluorescence), (b) pAC08 (GFP alone), or (c) pBTK570 (E2-crimson alone). (PDF)</p