Philippa Kaur

Plants increase their freezing resistance upon exposure to low temperatureThe freezing resistance (LT50 values) was found to vary ranging from − 6.9 °C (14-August-2017) to − 31.7 °C (04-November-2018) over the course of study period. The freezing resistance of leaves recorded during the 12 sampling time-points has been provided in Table 1 (also see39). The overlap of confidence intervals around the mean was examined for comparison of LT50 values for the different sampling time-points. Significant differences in freezing resistance were observed across the sampling time-points (Table 1). Leaves of R. anthopogon collected during summer [July and August (Air temperature and photoperiod was about 9.6 °C and 13 h day−1 respectively)] showed marginal resistance to freezing (LT50: − 7 °C) and thus, are more susceptible to freezing damage. Further, as the ambient air temperature and photoperiod decreased towards the end of growing season (i.e., October and November 2017 with air temperature and photoperiod of about − 1.1 °C and 10.5 h day−1 respectively), the plants acquired the highest freezing resistance (LT50: − 30 °C). Interestingly, a sharp increase in freezing resistance (− 29.4 °C) was observed in September 2018, when the daily mean air temperature decreased below 0 °C due to sudden snowfall (Supplementary Fig. S2). Comparison of LT50 values of all the leaf samples of R. anthopogon showed that cold de-acclimation occurred after the snowmelt during early spring in June (LT50: − 13.4 °C) with an increase in air temperature and photoperiod. These results demonstrated that R. anthopogon plants exhibit lowered freezing resistance during the warmer months [hence, these time-periods were referred as non-acclimation (NA)], progressively develop greater freezing resistance during the onset of winter season (hence, referred as cold acclimation) followed by an intermediate level of freezing resistance during the spring [hence, these time-periods were referred as de-acclimation (DA)].Table 1 The estimates of LT50, calculated by fitting sigmoidal curve to electrolyte leakage values of temperature treatments, recorded for leaves collected during the different sampling time-points (from August 22, 2017 to September 18, 2018).Full size tableDuring the acclimation period (i.e., late in the growing season), plants acquired the highest resistance to freezing (Fig. 1). The low electrolyte leakage (= high freezing resistance) observed during this period might be due to changes in cell wall properties (such as increase in lignification and suberization of cell walls), which provide resistance to diffusion of electrolytes from cells of the leaves to the extracellular water47. Moreover, high freezing resistance may also be attributed to high leaf toughness and sclerophyllous habit of this evergreen species48. Further, it was found that freezing resistance was the lowest during mid-summer period. This pattern could be explained by a trade-of between plant growth rates and freezing resistance, where warmer temperatures favour plant allocation to growth49. These observations corroborated well with earlier reports that showed a rapid increase in ‘freezing resistance’ during the transition from summer to early winter and vice versa50.Figure 1LT50 [black point (with solid fill) on the curve] calculated by fitting sigmoidal curve to relative electrolyte leakage (REL %) values recorded during the three different acclimation phases. GOF indicates ‘goodness of fit’ test values for the fitted sigmoidal curves.Full size imagePhotosynthetic rates are higher during non-acclimation and de-acclimation periodIt was found that PN of R. anthopogon varied in the range from 8.336 to 17.64 μmol(CO2)m−2 s−1 and E from 2.281 to 4.912 mol(H2O)m−2 s−1, throughout its growing season. The Gs of leaves was estimated to be in the range from 0.110 to 0.265 mol (H2O) m−2 s−1. WUE, a ratio of PN and E, varied between 52.21 and 87.68 (Table 2). The gas exchange parameters of R. anthopogon varied significantly among the sampling time-points [referred to here as different acclimation phases of the growing period of evergreen shrub (Fig. 2, Table 3)]. In particular, PN was significantly lower on 18-September-2018 (referred as cold acclimation phase), whereas it was higher on 31-August-2018 and 15-June-2018 (referred as NA and DA phases, respectively). Similarly, Gs of leaves was significantly lower during cold acclimation in comparison to the rest of the acclimation phases (i.e., NA and DA). Further, WUE was significantly higher during cold acclimation, while it was lower during both NA and DA (p ≤ 0.05) (Fig. 2).Table 2 Variability in leaf gas exchange parameters of R. anthopogon during the different acclimation phases (NA = Non-acclimation, LA = Late cold acclimation and DA = De-acclimation).Full size tableFigure 2Variability in leaf gas exchange parameters of R. anthopogon during the three acclimation phases [i.e., Non-acclimation (31 August, 2018), Cold acclimation (18 September, 2018) and De-acclimation (15 June, 2018)]. Different alphabets (a, b, c) represent statistically significant values (p  More

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We confirm that Cuba is home to the world’s largest European honeybee population that has naturally become Varroa-resistant, with an estimated 220,000 colonies being maintained without any form of chemical treatment for over two decades19 although some drone-trapping occurred during the early years of the transition period This is despite the presence of the K-haplotype of the mite20 and the widespread occurrence of DWV19 throughout Cuba. Hence, the Cuban honeybee population is the first major case of Varroa-resistant European bees occupying an entire country of a large size (109,884 km2). In Europe the proportion of varroa-resistant honeybee populations in each country is highly variable21,22, but they still consist of small, isolated populations within any country. For example, the second largest known area of European Varroa-resistant honeybees is in North Wales, UK where 104 beekeepers have managed around 500 honey bee colonies over an area of 2500 km2 without treatment for over a decade23.It has long been established that sub-Sharan African and Africanised honeybees are Varroa-resistant and both populations cover much larger areas than Cuba, but these honeybee races are not capable of thriving in temperate regions or are rejected by beekeepers in Northern hemispheres. However, previous studies on African/Africanised and European honeybees4,5,6,9 all appear to have evolved with the same resistance mechanism7 and Cuban honeybees follow this pattern showing high recapping behaviour, high mite removal behaviour and low mite reproduction (Figs. 1, 4, Table 1).The strongest evidence that increased recapping behaviour is a direct response to the presence of Varroa, is the very low recapping rates in Varroa-naïve colonies. This is evidenced by the recapping baseline data that has now been collected from four different Varroa-naïve (Varroa free) honeybee populations (Australia, UK [two populations] and Hawaii [this study]) all producing similar results (Fig. 1). Across the four populations, a total of 9542 worker cells from 15 colonies have been studied with an average recapping rate of 2.0% (+ SD 3.2). Interestingly, only two of the colonies had atypical recapping rates of 8.5% and 10.7%, from Australia and Kauai respectively. This may suggest increased sensitivity in these colonies as no obvious causes e.g., wax moth or dead pupa, were detected in either colony. The data summary in Fig. 1 indicates that even in Varroa-treated populations the workers are still able to detect mite infested cells, but the average consistently falls significantly below that found in resistant populations. That is, in non-infested worker cells recapping rates are significantly higher in resistant populations in comparison to susceptible populations (Fig. 1) t4, 5 = − 4.185, p = 0.0023 as well as for infested cells t4, 5 = − 6.905, p = 0.00007.The ability of Cuban honeybees to detect infested cells causes not only high recapping levels but also high removal rates of artificially mite-infested cells. A mean removal rate of 81% is among one of the highest recorded in Apis mellifera7. The average control rate of 45% is driven by three colonies that all removed more than 75% of the controls, while the average of the remaining seven colonies was 28%. During the mite-removal studies in March 2022 natural Varroa infestation was 23%, whereas in December 2021 it was only 13%. This is due to decreasing worker brood rearing, caused by a shortage of nectar during the annual dry season. During this time there is an increase in hygienic behaviour in the colonies24, which could help explain the higher-than-expected removal of control cells.The reproductive ability of Varroa to produce viable i.e., mated, female offspring (r) in infested worker cells in resistant colonies in South Africa4 (r = 0.9), Brazil4 (r = 0.8), Mexico18 (r = 0.73), Europe3 (r = 0.84) is similar to the 0.87 found in Cuba (this study). In Cuba ‘r’ reduces to 0.77 when both single and multiple infested cells are considered. This reduction in mite reproduction, relative to susceptible colonies that have values of r greater than one, is directly linked to the increased ability of resistant workers to both detect and remove, by cannibalisation, the infested pupa. Hence, this ensures the invading mite fails to reproduce7 or reduces mite fertility due to the recapping process4. Although, in this study no significant difference was found in the reproduction of Varroa in recapped or non-recapped cells, supporting the findings of two previous studies5,9. Therefore, recapping may be playing a minor role in resistance. However, recapping remains the best indicator or ‘proxy’ of resistance within the vast majority of honeybee populations since it’s easier, quicker, and it requires less skill to measure recapping rates than mite removal rates. However, recapping is a highly variable trait7, hence both many cells (200–300) per colony and many colonies ( > 10) per population ideally need to be studied to help reduce the variablity, also in temperate countries measuring recapping when mite-infestation rates peak in autumn maximises detecting infested cells since the recapping of cells is spatially associated with infested cells11.Despite the current focus on what is happening in worker cells, studies focusing on the role of recapping in drone brood are still in their infancy with. Currently, data is only available from South Africa9 (Fig. 1) and now Cuba (this study). Interestingly, both studies indicate no significant difference in recapping rates between infested and non-infested brood. This is caused by some colonies performing no recapping of drone brood, while some colonies do recap cells but in a non-targeted manner. Whereas there is a significant increase in the size of the recapped area between infested (3.1 mm) and non-infested (2.3 mm) worker cells (Fig. 3), this does not occur in drone brood, as it appears that the holes are entirely exploratory. However, the lack of removal of infested drone brood may be playing an important role in mite-resistance (see below).The mite infestation of worker cells currently varies between 23 and 13% in Cuba (this study), roughly 25 years after it was first detected (1996). Whereas, in Mexico and Brazil, infestation rates of worker brood have fallen from around 20% in 1996/1999 down to 4% in 2018/197. Although, Varroa was first detected in Brazil much earlier, in 197225 and the Africanised honeybees adapted to the mite and spread northward replacing the susceptible European colonies. Therefore, we predict that the worker infestation rate in Cuba will continue to fall over the next 20 years, especially if high mite-removal rates persist. Correspondingly, we would expect to see the infestation rates of the drone brood (currently at 40%) to remain high as mites potentially avoid reproduction in worker cells. This potentially is a key, but currently overlooked part, of the resistance mechanism. Since an empirical model26 indicated that negative mite population growth occurs in (resistant) Africanised honeybee colonies only when the initial drone cells are present. This is thought to arise because mites also show a tenfold preference to reproduce in drone cells (which comprises only 1–5% of all the honeybee brood) and they soon become overcrowded as the mite population increases. This leads to inter-mite competition for the limited food and space, causing an increase in mite mortality27, resulting in negative reproductive success for mites entering these overcrowded drone cells. Thus, mite population growth in drone brood cells is limited by a density-dependent mechanism. In Cuba it has been observed that strong colonies typically with drone brood do not weaken during the drought season, whereas colonies without drone brood are weak and often die during the drought (APP personal comm).Although Cuban beekeepers have been aware of their mite-resistant honeybees for 15 to 20 years’, Cuba’s situation has only recently come to light16,18. The main reason for Varroa-resistance in Cuba is due to the centralised decision to allow natural resistance to evolve, as also was done successfully in South Africa3, rather than becoming locked into using miticides, as has happened throughout the Northern hemisphere. The CIAPI and Veterinarian Services central decision to ‘not treat’ was greatly assisted by all Cuban beekeepers being professional, registered and embedded within a strong locally based beekeeping community where colony movement and exchange of queens is within each province.There is also a large feral population and due to Cuba’s sub-tropical climate, queens are replaced annually in managed colonies because of almost continuous egg-laying, similar to honeybees in Hawaii. This rapid queen turnover speeds up natural selection relative to honeybee populations in more temperate climates. Finally, Cuba’s 60-year ban on honeybee importation has helped isolate the country from been invaded by Africanised bees which has occurred in many nearby regions (eg. Mexico, Southern USA, Puerto Rico, neighbouring Dominican Republic13 and Haiti (D. Macdonald, Apiary Inspector, Min. of Agi BC, Canada, pers. Comm.). Cuba has many managed European colonies coupled with many queen rearing stations. These colonies are productive and mild mannered. Thus, Cuba is an excellent example of the power of natural selection in honeybees when they are allowed to adapt naturally to Varroa with minimal human interference. More

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Iron and carbon dependent mutualism between Chlorella sorokiniana and Ralstonia pickettii forms a synthetic phototrophic communityThe synthetic microalgal-bacterial community based on the active exchange of iron and carbon was developed by screening multiple siderophore producer bacteria and dye decolorizer algae (Fig. 1; refer to Supplementary Data S1 for detailed results). Out of seven bacterial isolates obtained from untreated textile wastewater, five showed relatively high siderophore production in CAS agar plates and broth (Fig. S1). In broth, Serratia plymuthica PW1, Serratia liquefaciens PW71, and Ralstonia pickettii PW2 produced siderophores in decreasing order of concentration, i.e., 15.26 ± 1.3  > 13.28 ± 0.9  > 10.85 ± 0.7 µMmL−1 (Table 1). Arnow’s assay confirmed that S. plymuthica PW1 (81.10 ± 9.8 µMmL−1), R. pickettii PW2 (97.43 ± 16.8 µMmL−1), and S. liquefaciens PW71 (103.1 ± 8.3 µMmL−1) produced catecholate-type siderophores. On the other hand, Csaky’s assay confirmed that Stenotrophomonas maltophilia PW5 (37.86 ± 0.4 µMmL−1) and Stenotrophomonas maltophilia PW6 (17.73 ± 0.2 µMmL−1) produced hydroxamate-type of siderophores. Out of the five algal species, only freshwater microalgae Chlorella sorokiniana and Scenedesmus sp. showed the highest dye degradation potential; therefore, they were selected for further experiments (Data S1).Fig. 1: The study design explains different stages of experiments to develop a phototrophic community of previously non-associated algae and bacteria.The stages include (A) isolation of bacterial strains from textile wastewater collected from Panipat Industrial area, Haryana (India); B cultivation of freshwater and marine algal strains; C assessment of siderophore production in bacterial strains using Schwyn and Neilands’s universal Chrome Azurol S (CAS) assay; D assessment of dye degradation potential of algae strains using Acid Black 1 (AB1) dye; E interaction study between siderophore producing bacteria and dye degrader microalgae to identify bacterial strains that could sustain on algae-derived DOM secreted in algal exudates; F algal-bacterial co-culturability assessment to study different types of microbial interactions viz. antagonism, mutualism, or no interaction between the two organisms, and G identification of algal-bacterial model phototrophic community based on the active exchange of iron and DOM (refer to Data S1 for detailed results).Full size imageTable 1 Characterization of siderophore production in bacterial strains isolated from textile wastewater.Full size tableAfter that, the sterile exudates from C. sorokiniana and Scenedesmus sp. were used as the sole source of dissolved organic matter for bacterial growth and selection of appropriate microalgal-bacterial partners comprising the phototrophic community (Fig. 1E; Data S2). All five bacterial isolates grew well on the exudate of C. sorokiniana as a sole source of carbon. On the contrary, on exudates of Scenedesmus sp., S. plymuthica PW1 showed moderate growth in 20 h, while the growth of R. pickettii PW2 and S. liquefaciens PW71 remained insignificant. S. maltophilia PW5 and PW6 failed to grow in the exudate of Scenedesmus sp. (Fig. S2B).Finally, the compatibility between the phototrophic community of selected microalgae (C. sorokiniana/ Scenedesmus sp.) and siderophore-producer bacteria (S. plymuthica PW1/ R. pickettii PW2/ S. liquefaciens PW71) was tested by co-culturing them in iron limiting BBM media (BBM-Fe; without EDTA) (Fig. 1F). In the absence of EDTA, Fe precipitates rapidly as iron oxyhydroxides and becomes unavailable to microbes. Microalgal growth curves in co-culture assays were used to measure and compare population characteristics such as carrying capacity ‘k’, growth rate ‘r’, etc., in axenic and consortium setups. Algal growth parameters in co-culture with a bacterial partner were used to categorize their interaction as putative mutualistic, antagonistic, and neutral (Data S1, Tables S1 and S2) [42]. Under iron-limiting conditions, axenic C. sorokiniana experienced iron stress as the cell growth was 4.2 ± 0.4 × 106 cells mL−1 after 200 h incubation. On the other hand, axenic Scenedesmus sp. showed a significantly higher growth (11.3 ± 1.2 × 106 cells mL−1) than C. sorokiniana suggesting an effective iron uptake mechanism under iron-limiting conditions (k; t-test, p = 0.001) (Table S1). In contrast to the axenic microalgal culture, C. sorokiniana in co-culture with R. pickettii PW2 showed a significant increase in cell count at 200 h (6.2 ± 0.85 × 106 cells mL−1) (auc; p = 0.000). However, S. plymuthica PW1 exerted a negative effect on C. sorokiniana (Fig. 2A), as indicated by its significant increase in doubling time (p = 0.009) and reduction in auc (p = 0.001) (Fig. 3A). While S. liquefaciens PW71 remained neutral to C. sorokiniana (auc; p = 0.430) (Fig. 2A, Table 2). On the other hand, the interaction of Scenedesmus sp. with both R. pickettii PW2 and S. liquefaciens PW71 was neutral, while S. plymuthica PW1 showed a negative effect (Figs. 2A and 3A).Fig. 2: Assessment of algal and bacterial growth in co-culture experiments.A The growth curves represent the difference in the growth of C. sorokiniana when grown axenically or in co-culture with S. plymuthica PW1, R. pickettii PW2, and S. liquefaciens PW71 under iron limiting conditions. Whereas, the effect of bacteria on the growth of Scenedesmus sp. was less prominent. The difference in the CFUs of bacterial strains in axenic culture and co-culture suggests the growth-promoting effect of C. sorokiniana on S. plymuthica PW1 and R. pickettii PW2. B Anion-exchange chromatography suggests a difference in the glycosyl composition in the EPS of C. sorokiniana and Scenedesmus sp. C The area under curve (auc) of S. plymuthica PW1 and R. pickettii PW2 obtained after growth curves in different sugars. Here, ‘a’, ‘b’, etc., represent grouping after Tukey’s post hoc test.Full size imageFig. 3: Assessment of algal growth parameters in the algal-bacterial phototrophic community under iron-limiting conditions.A The confidence interval plots represent the significant difference in the growth parameters i.e., growth rate ‘r’, carrying capacity ‘k’, doubling time ‘Dt’, and area under curve ‘auc’, of C. sorokiniana (left panel) and Scenedesmus sp. (right panel) in algal-bacterial co-cultures w.r.t. to axenic culture (horizontal blue dashed line). The symbols ‘*’ and ‘**’ represent p values with statistical significance of ‘p  More

Insect rearingThe B. dorsalis strain collected from a carambola (Averrhoa carambola) orchard in Guangzhou, Guangdong Province, was reared under laboratory conditions (27 ± 1 °C, 12:12 h light:dark cycle, 70–80% RH). A maize-based artificial diet containing 150 g of corn flour, 150 g of banana, 0.6 g of sodium benzoate, 30 g of yeast, 30 g of sucrose, 30 g of paper towel, 1.2 mL of hydrochloric acid and 300 mL of water was used to feed the larvae. Adults were fed a solid diet (consisting of 50 g yeast and 50 g sugar) and 50 mL sterile water in a 35 cm × 35 cm × 35 cm wooden cage. For B. dorsalis, the female will start laying eggs once mated and the female will start mating 7 days after emergence. To make sure all females used in our study were gravid females, females were selected 10 day after emergence.Visualization of CF-BD with FISH and PCRFISH was carried out on dissected gut and ovary samples from B. dorsalis. The hybridization protocol for the gut and ovary was performed according to a previously described method32. Briefly, the gut and ovary were collected and immediately soaked in Carnoy’s fixative for 12 h. After sample fixation, proteinase K (2 mg/mL) treatment for 20 min at 37 °C and HCl (0.2 mol/L) treatment for 15 min at room temperature were performed successively. Then, followed by dehydration in ethanol, the samples were incubated in buffer (20 mM Tris-HCl (pH 8.0), 0.9 M NaCl, 0.01% sodium dodecyl sulfate, 30% formamide) containing 50 nM CF-BD specific probe (5′-AATGGCGTACACAAAGAG-3′) labeled with cy3 at the 5′ end for 90 min. After incubation, the samples were washed with buffer (0.1 M NaCl, 20 mM Tris/HCl (pH 8.0), 5 mM ethylenediaminetetraacetic acid (pH 8.0), 0.01% SDS) and observed under an epifluorescence microscope (Axiophot, Carl Zeiss, Shinjuku-ku, Japan).To further confirm CF-BD in rectum and ovary of mature females, rectums and ovaries of mature females were dissected and fixed in formalin fixation for 24 h. After soaking in graded alcohols and xylene, all samples were embedded in paraffin for section preparation. Samples were sliced into 4 µm each before pasting on the glass slide and then sent for FISH with the same probe (labeled with cy3 at the 5′ end) used above. Moreover, nested PCR was applied to detect CF-BD in 19 ovaries of mature females according to the method of Guo et al., 201733. Briefly, a 1149 bp region of gyrB gene of CF-BD was amplified by the specific outer primer gyrBP1-F (5′-CAGCCCACTCTGAACTGTAT-3′) and gyrBP1-R (5′-TCAGGGCGTTTTCTTCGATA-3′) under a temperature profile of 95 °C for 1 min, which was followed by 25 cycles of 95 °C for 30 s, 52 °C for 30 s, 72 °C for 90 s, and 72 °C for 5 min. Then, a 371 bp region of the gyrB gene of CF-BD was amplified by the specific inner primer gyrBP4-F (5′-ACGCTGGCTGAAGACTGCC-3′) and gyrBP4-R (5′-TGGATAGCGAGACCACGACG-3′) under a temperature profile of 95 °C for 2 min, which was followed by 35 cycles of 95 °C for 30 s, 57 °C for 30 s, 72 °C for 30 s, and 72 °C for 5 min.Influence of CF-BD on B. dorsalis ovary developmentTo evaluate the effect of CF-BD on ovary development, newly emerged B. dorsalis females were injected with streptomycin and CF-BD suspension (both dilute in sterile water). Specifically, 10 µL 25% glycerol solution containing CF-BD was added into 100 mL Luria-Bertani (LB) liquid medium and culturing for 1 day by shaking (180 rpm) in 30 °C incubator. After culturing, CF-BD was collected by centrifuging (3000 rpm, 15 min) the medium in a 50 mL centrifuge tube. Then collected CF-BD was re-suspended with 5 mL sterile water. CF-BD concentration was measured on a hemocytometer and CF-BD concentrations used in the following assays were prepared by diluting the original concentration with sterile water. A 0.5 mm inside diameter capillary needle with 1 μL streptomycin or CF-BD suspension was used for injection. The injection operation was carried out on a microinjector (Eppendorf FemtoJet), and every female was injected in the abdomen near the ovipositor. The concentrations of streptomycin used were 20 mg/mL, 10 mg/mL and 5 mg/mL, respectively. And CF-BD suspension concentrations were 3 × 107 cfu/mL, 1.5 × 107 cfu/mL and 7.5 × 106 cfu/mL, respectively. For control, the female fly was injected with 1 μL sterile water in the abdomen near the ovipositor. Then the development level of the ovary was assessed by comparing the width and length of ovary between streptomycin (or CF-BD suspension) injection flies and control. For CF-BD injected flies, developmental facilitation was observed for ovaries 2 days before the flies reached sexual maturity (flies will reach sexual maturity after 7 days). For antibiotic injected flies, ovaries were dissected after 7 days.Oviposition assaysThe method reported in previous studies was followed for the oviposition experiments17. Briefly, a 2-choice apparatus was assembled in a cage made up of wood and wire gauze (length: width: height = 60 cm: 60 cm: 60 cm) with two petri dishes (diameter: 3 cm) at the bottom of the cage (Fig. 2a). All devices were sterilized before each experiment. Fresh fruits of guava (Psidium guajava Linn.) and mango (Mangifera indica L.) were sourced from the local market in Guangzhou, China. These fruits were sterilized on the surface with ethanol and ground into puree with a sterilized grinder, and puree (2 g) was added to the sterilized Petri dishes of the cages (one dish with puree containing 100 μL CF-BD (0.8*108 cfu/mL) in sterile water, and one dish with puree containing 100 μL sterile water). Then the prepared cages were divided into two groups for different assays. Group 1: At 0 h, 50 gravid females of B. dorsalis were placed in the cages and egg numbers in the petri dishes were recorded after 2 h. Group 2: At 4 h, 50 gravid females of B. dorsalis were placed in the cages and egg numbers in the petri dishes were recorded after 2 h.To test the oviposition attraction of 3-HA, a 4-choice apparatus was assembled in a cage made up of wood and wire gauze (length: width: height = 60 cm: 60 cm: 60 cm) with four petri dishes (diameter: 3 cm) at the bottom of the cage. In the Petri dishes, 2 g puree, 2 g puree + 0.2 mg 3-HA, 2 g puree + 2 mg 3-HA and 2 g puree + 20 mg 3-HA were added. Then, the egg-laying behavior was observed31.To test the oviposition attraction of 3-HA to flies with genes knocked down, 20 females injected with dsRNA were placed into the above cage with two Petri dishes. In the Petri dishes, 2 g guava puree and 2 g guava puree + 20 mg 3-HA were added. Then, the egg-laying behavior was observed using the above method. Oviposition of normally reared females was performed as a control. The oviposition index was calculated using the following formula:Oviposition index = (O − C)/(O + C), where O is the number of eggs in the treatment and C is the number of eggs in the control.Volatile analysisThe volatile compounds in guava and mango purees were analyzed by GC–MS according to the method described in a previous study17. Briefly, 2 g puree mixed with sterile water or CF-BD was added into a 20 ml bottle, and then a 100-μm polydimethylsiloxane (PDMS) SPME fiber (Supelco) was used to extract the headspace volatiles for 30 min. GC–MS was performed with an Agilent 7890B Series GC system coupled to a quadruple-type-mass-selective detector (Agilent 5977B; transfer line 250 °C, source 230 °C, ionization potential 70 eV). The 3-HA concentrations in puree mixed with sterile water and CF-BD were measured with the standard curve drawn by the authentic standards of 3-HA. And 3-HA concentration in puree mixed with sterile water and CF-BD was compared with a paired sample Student’s t-test.Olfactometer bioassaysAn olfactometer consisting of a Y-shaped glass tube with a main arm (20 cm length*5 cm diameter) and two lateral arms (20 cm length, 5 cm diameter) was used. The lateral arms were connected to glass chambers (20 cm diameter, 45 cm height) in which the odor sources were placed. To ensure a supply of odor-free air, both arms of the olfactometer received charcoal-purified and humidified air at a rate of 1.3 L/min.To test the attraction effect of puree supplemented with CF-BD or 3-HA for females, puree mixed with CF-BD was prepared and placed in one odor glass chamber. In the control odor glass chamber, puree mixed with sterile water was placed. After 4 h, gravid females were individually released at the base of the olfactometer and allowed 5 min to show a selective response. The response was recorded when a female moved >3 cm into one arm and stayed for >1 min. Females that did not leave the base of the olfactometer were recorded as nonresponders. Only females that responded were included in the data analysis. Odor sources were randomly placed in one arm or the other at the beginning of the bioassay, and the experiment was repeated ten times. The system was washed with ethanol after every experiment. More than 100 females were selected for testing, and each female was used only once for each odor. A chi-square test was performed to compare the attraction difference between puree mixed with sterile water and CF-BD.Olfactory trap assaysThe attraction of purees supplemented with CF-BD to mature females was also tested. The test chamber was assembled with a plastic cylinder (120 × 30 cm) covered by a ventilated lid. The test chamber contained an odor-baited trap (2 g puree + 100 μL CF-BD (0.8*108 cfu/mL)) and a control trap (2 g puree + 100 μL sterile water). The traps were made of transparent plastic vials (20 × 6 cm) and were sealed with a yellow lid on which small entrances were present to let the flies in (Fig. 3a). After 0 h or 4 h of fermentation, 100 gravid females were released in the cage. The fly number in each trap bottle was recorded after 2 h. The number of flies was compared with a paired sample Student’s t-test.The attraction effect of puree supplemented with 3-HA on mature females was tested by placing four traps (2 g puree, 2 g puree + 0.2 mg 3-HA, 2 g puree + 2 mg 3-HA and 2 g puree + 20 mg 3-HA) in the test chamber. Then, the attraction effect was observed31.Video observation of egg-laying behaviorEgg-laying behavior was observed in a Petri dish. Briefly, guava puree was added to a centrifuge tube on which a hole was made. Then, one gravid female was placed into the petri dish, and the lid was closed. Above the petri dish, a camera was placed to record the behavior of the female before laying eggs.EAG analysisEAG analysis was performed to determine whether 3-HA could elicit electrogram responses in the ovipositors of gravid females and Obps knocked down gravid females. For EAG preparations, the ovipositor of a gravid female was cut off and mounted between two glass electrodes (one electrode connected with the ovipositor tip). The ovipositor tip was cut slightly to facilitate electrical contact. Dilution of 3-HA in ethanol (0.1, 1 and 10 mg/mL) was used as a stimulant. Ethanol was used as control. For each ovipositor, ethanol and 3-HA diluted in ethanol were used as stimulants. The signals from the ovipositors were analyzed with GC-EAD 2014 software (version 4.6, Syntech).Transcriptome sequencing and gene identificationTo identify the olfactory genes that contribute to B. dorsalis oviposition preference, the transcriptome sequencing results of the female ovipositors at different developmental times (0 day, 3 days, 6 days, 9 days and 12 days) were compared. For each time, 5 ovipositors were dissected for RNA extraction. In addition, five replicates were included for each time. In the next step, paired-end RNA-seq libraries were prepared by following Illumina’s library construction protocol. The libraries were sequenced on an Illumina HiSeq2000 platform (Illumina, USA). FASTQ files of raw reads were produced and sorted by barcodes for further analysis. Prior to assembly, paired-end raw reads (uploaded to National Genomics Data Center, Accession number: PRJCA004790) from each cDNA library were processed to remove adapters, low-quality sequences (Q  More

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