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Study siteThe study was conducted in the Brasília National Park (PNB), Federal District, Brazil (15º39′57″ S; 47º59′38″ W), a 42.355 ha Protected Area with a typical vegetation configuration found in the Cerrado of the central highlands of Brazil, i.e., a mosaic of gallery forest patches along rivers surrounded by a matrix of savannas and grasslands34. The climate in the region falls into the Aw category in the Köppen scale, categorizing a tropical wet savanna, with marked rainy (October to March) and dry (April to September) seasons.We carried out the study in eight fixed sampling sites scattered evenly throughout the PNB and separated by at least two kilometers from one another (Supplementary Fig. S1). The sites consisted of four cerrado sensu stricto sites (bushy savanna containing low stature trees); two gallery forest edges sites (ca. 5 m from forest edges, containing a transitional community), and two gallery forest interior sites. These three types reflect the overall availability of habitat types in the reserve (excluding grasslands) and are the most appropriate foraging areas to sample interactions as bat-visited plants are either bushes, trees, or epiphytes, but rarely herbs35.Bat and interaction samplingsWe sampled bat-plant interactions using pollen loads collected from bat individuals captured in the course of one phenological year, thus configuring an animal-centered sampling. We carried out monthly field campaigns to capture bats from October 2019 to February 2020, from August to September 2020, and from March to July 2021. In each month, we carried out eight sampling nights during periods of low moonlight intensity, each associated with one of the eight sites. Each night, we set 10 mist nets (2.6 × 12 m, polyester, denier 75/2, 36 mm mesh size, Avinet NET-PTX, Japan) at ground level randomly within the site, which were opened at sunset and closed after six hours. We accumulated a total sampling effort of 552 net-hours, 28,704 m2 of net area, or 172,224 m2h sensu Straube and Bianconi36.All captured bats were sampled for pollen, irrespective of family or feeding guild. We used glycerinated and stained gelatin cubes to collect pollen grains from the external body of bats (head, torso, wings, and uropatagium). Samples were stored individually, and care was taken not to cross-contaminate samples. Pollen types were identified by light microscopy, and palynomorphs were identified to the lowest-possible taxonomical level using an extensive personal reference pollen collection from plants from the PNB (details in next section). Palynomorphs were sometimes classified to the genus or family level or grouped in entities representing more than one species. Any palynomorph numbering five or fewer grains in one sample was considered contamination, alongside any anemophilous species irrespective of pollen number.Bats were identified using a specialized key37 and four ecomorphological variables were measured for each individual. (i) Forearm length and (ii) body mass were used to calculate the body condition index (BCI), a proxy of body robustness38, where higher BCI values indicate larger and heavier bats, which are less effective in interacting with flowers in general due to a lack of hovering behavior, the incapability of interacting with delicate flowers that cannot sustain them, a lower maneuverability and higher energetic requirements39. Moreover, we measured (iii) longest skull length (distance from the edge of the occipital region to the anterior edge of the lower lip) and (iv) rostrum length (distance from the anterior edge of the eye to the anterior edge of the lower lip) to calculate the rostrum-skull ratio (RSR), a proxy of morphological specialization to nectar consumption23. Higher RSR values indicate bats with proportionally longer rostra in relation to total skull length. Longer rostra in bats are associated with a weaker bite force and thus less effective in consuming harder food items such as fruits and insects, thus suggesting a higher adaptation to towards nectar40,41. Bats were then tagged with aluminum bands for individualization and released afterward. To evaluate the sampling completeness of the bat community and of the pollen types found on bats, we employed the Chao1 asymptotic species richness estimator and an individual-based sampling effort to estimate and plot rarefaction curves, calculating sampling completeness according to Chacoff et al.42.All methods were carried out in accordance with relevant guidelines and regulations. The permits to capture, handle and collect bats were granted by the Ethical Council for the Usage of Animals (CEUA) of the University of Brasília (permit 23106.119660/2019-07) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) (permit: SISBIO 70268). Vouchers of each species, when the collection was possible, were deposited in the Mammal Collection of the University of Brasília.Assessment of the plant communityIn each of the eight sampling sites, we delimited a 1000 × 10 m transect, each of which was walked monthly for one phenological year (January and February 2020, August to December 2020, and March to July 2021) to build a floristic inventory of plants of interest and to estimate their monthly abundance of flowering individuals. Plant species of interest were any potential partner for bats, which included species already known to be pollinated by bats, presenting chiropterophilous traits sensu Faegri and Van Der Pijl43, or any plant that could be accessed by and reward bats, whose flowers passes all the three following criteria:(i) Nectar or pollen is presented as the primary reward to visitors. (ii) Corolla diameter of 1 cm or more. This criterion excludes small generalist and insect-pollinated flowers where the visitation by bats is mechanically unlikely. It applies to the corolla diameter in non-tubular flowers or the diameter of the tube opening. Exceptions were small and actinomorphic flowers aggregated in one larger pollination unit (pseudanthia) where the 1 cm threshold was applied to inflorescence diameter. (iii) Reward must be promptly available for bats. This criterion excludes species with selective morphological mechanisms, such as quill-shaped bee-pollinated flowers or flowers with long and narrow calcars.All flowering individuals of interest species found in the transects were registered. A variable number of flowers/inflorescences (n = 5–18) were collected per species for morphometric analysis. For each species, we calculated floral tube length (FTL), corresponding to the distance between the base of the corolla, calyx, or hypanthium (depending on the species) to its opening, and the corolla’s outermost diameter (COD), which corresponds to the diameter of the corolla opening (tubular flowers) or simply the corolla diameter (non-tubular flowers). For pseudanthia-forming species, inflorescence width was measured. Pseudanthia and non-tubular flowers received a dummy FTL value of 0.1 mm to represent low restriction and enable later calculations. Finally, we collected reference pollen samples from all species from anthers of open flowers, which were used to identify pollen types found on bats. For plant species found in pollen loads but not in the PNB, measures were taken from plants found either on the outskirts of the site (Inga spp.) or from dried material in an online database (Ceiba pentandra, in https://specieslink.net/) using the ImageJ software44. Vouchers were deposited in the Herbarium of the Botany Department, University of Brasília.Data analysisNetwork macrostructureWe built a weighted adjacency matrix i x j, where cells corresponded to the number of individuals of bat species i that interacted with plant species or morphotype j. All edges corresponding to legitimate interactions were included. With this matrix, we calculated three structural metrics to describe the network’s macrostructure. First, weighted modularity (Qw), calculated by the DIRTLPAwb + algorithm45. A modular network comprises subgroups of species in which interactions are stronger and more frequent than species out of these subgroups10, which may reveal functional groups in the network9. Qw varies from zero to one, the latter representing a perfectly modular network.Second, complementary specialization through the H2′ metric46. It quantifies how unique, on average, are the interactions made by species in the network, considering interaction weights and correcting for network size. It varies from zero to one, the latter corresponding to a specialized network where interactions perfectly complement each other because species do not share partners.Lastly, nestedness, using the weighted WNODA metric25. Nested networks are characterized by interaction asymmetries, where peripheral species are only a subset of the pool of species with which generalists interact47. The index was normalized to vary from zero to one, with one representing a perfectly nested network. Given that the network has a modular structure, we also tested for a compound topology, i.e., the existence of distinct network patterns within network modules, by calculating intra-module WNODA and between-module WNODA36. Internally nested modules appear in networks in which consumers specialize in groups of dissimilar or clustered resources and suggest the existence of distinct functional groups of consumers25,48. Metric significance (Qw, H2′, and WNODA) was assessed using a Monte Carlo procedure based on a null model. We used the vaznull model3, where random matrices are created by preserving the connectance of the observed matrix but allowing marginal totals to vary. One thousand matrices were generated and metrics were calculated for each of them. Metric significance (p) corresponded to the number of times the null model delivered a value equal to or higher than the observed metric, divided by the number of matrices. The significance threshold was considered p ≤ 0.05.Given a modular structure, we followed the framework of Phillips et al.49 that correlates network concepts (especially modularity) with the distribution of morphological variables of pollinators to unveil patterns of niche divergence in pollination networks. Given the most parsimonious module configuration suggested by the algorithm, we compared modules in terms of the distribution of morphological variables of the bat (RCR and BCI) and plant (FTL and COD) species that composed the module. Differences between modules means were tested with one-way ANOVAs.Drivers of network microstructureThe role of different ecological variables in determining pairwise interaction frequencies was assessed using a probability matrices approach3. This framework considers that an interaction matrix Y is a product of several probability matrices of the same size as Y, with each matrix representing the probability of species interacting based on an ecological mechanism. Thus, adapting it to our objectives, we have Eq. (1):$$mathrm{Y}=mathrm{f}(mathrm{A},mathrm{ M },mathrm{P},mathrm{ S})$$
(1)
where Y is the observed interaction matrix, and a function of interaction probability matrices based on species relative abundances (A), representing neutrality as species interact by chance; species morphological specialization (M), phenological overlap (P), and spatial overlap (S). We built models containing each of these matrices in the following ways:Relative abundance (A): matrix cells were the products of the relative abundances of bat and plant species. The relative abundances of bats were determined through capture frequencies (each species’ capture frequency divided by all captures, excluding recaptures) and the relative abundances of plants were determined by the number of flowering individuals recorded in transections (each species’ summed abundance in all transects and all months divided by the pooled abundance of all species in the network). Cell values were normalized to sum one.Morphological specialization (M): cells were the probability of species interacting based on their matching degree of morphological specialization. Morphologically specialized bats (i.e., longer rostra and smaller size) are more likely to interact with morphologically specialized flowers (i.e., longer tubes and narrower corollas), while unspecialized bats are more likely to interact with unspecialized, accessible flowers. For this purpose, we calculated a bat specialization index (BSI) as the ratio between RCR and BCI, where higher BSI values indicate overall lower body robustness and longer snout length. Likewise, the flower specialization index (FSI) was calculated for plants as the ratio between FTL and COD, where higher values indicate smaller, narrower, long-tubed flowers that require specialized morphology and behavior from bats for visitation. BSI and FTL were normalized to range between zero and one and were averaged between individuals of each species of bat or plant. Therefore, interaction probabilities were calculated as in Eq. (2):$${P}_{i,j}=1-|{BSI}_{i}-{FSI}_{j}|$$
(2)
where Pi,j is the interaction probability between bat species i and plant species j and |BSIi – FSIj| is the absolute difference between bat and plant specialization indexes. Similar index values (two morphologically specialized or unspecialized species interacting) lead to a low difference in specialization and thus to a high probability of interaction (Pi,j → 1), whereas the interaction between a morphologically specialized and a morphologically unspecialized species leads to a high absolute difference and thus lower probability of interaction (Pi,j → 0). Cell values of the resulting matrix were normalized to sum one.Phenological overlap (P): cells were the probability of species interacting based on temporal synchrony, calculated as the number of months that individuals of bat species i and flowering individuals of plant species j co-occurred in the research site, pooling all capture sites/transections. Cell values were normalized to sum one.Spatial overlap (S): cells were the probability of species interacting based on their co-occurrence over small-scale distances and vegetation types, calculated as the number of individuals from a bat species i captured in sampling sites where the plant species j was registered in the transection, considering all capture months. Cell values were normalized to sum one.Because more than one ecological mechanism may simultaneously drive interactions3,9, we built an additional set of seven models resultant from the element-wise multiplication of individual probability matrices:

SP: The spatial and temporal distribution of species work simultaneously in driving a resource turnover in the community, driving interactions.

AS: Abundance drives interactions between bats and plants, but within spatially clustered resources in the landscape caused by a turnover in species distributions.

AP: Abundance drives interactions between bats and plants, but within temporally clustered resources caused by a seasonal distribution of resources.

APS: Abundance drives interactions between bats and plants, but within resource clusters that emerge by a simultaneous temporal and spatial aggregation.

MS: Similar to AS, but morphology drives interactions within spatial clusters.

MP: Similar to MP, but morphology drives interactions within temporal clusters.

MPS: Similar to APS, but morphology drives interactions within spatiotemporal clusters.

Finally, we created a benchmark null model in which all cells in the matrix had the same probability value. All the compound matrices and the null model were also normalized to sum one.To compare the fit of these probability models with the real data, we conducted a maximum likelihood analysis3,9. We calculated the likelihood of each of these models in predicting the observed interaction matrix, assuming a multinomial distribution for the probability of interaction between species12. To compare model fit, we calculated the Akaike Information Criterion (AIC) for each model and their variation in AIC (ΔAIC) in relation to the best-fitting model. The number of species used in the probability matrices was considered the number of model parameters to penalize model complexity. Intending to assess whether nectarivorous bats and non-nectarivorous bats assembly sub-networks with different assembly rules, we created two partial networks from the observed matrix. One contained nectarivores only (subfamilies Glossophaginae and Lonchophyllinae) and their interactions, and the other contained frugivore and insectivore bats and their interactions. We repeated the likelihood procedure for these two partial networks.To conduct the likelihood analysis, we excluded plant species from the network that could not have their interaction probabilities measured, such as species found in pollen samples but not registered in the park or pollen types that could not be identified to the species level. Therefore, the interaction network Y and probability matrices did not include these species (details in Supplementary Table S1).SoftwareAnalyses were performed in R 3.6.050. Network metrics and null models were generated with the bipartite package51, and the sampling completeness analysis was performed with the vegan package52. Gephi 0.9.253 was used to draw the graph. More

Insect rearingWT B. dorsalis were collected from Haikou, Hainan province, China, in 2008. They were maintained at the Key Laboratory of Entomology and Pest Control Engineering in Chongqing at 27 ± 1 °C, 70 ± 5% relative humidity, with a 14-h photoperiod. Adult flies were reared on an artificial diet containing honey, sugar, yeast powder, and vitamin C. Newly hatched larvae were transferred to an artificial diet containing corn and wheat germ flour, yeast powder, agar, sugar, sorbic acid, linoleic acid, and filter paper.Behavioral assaysDouble trap lure assays were set up to compare the olfactory preferences of gravid and virgin females in a 20 × 20 × 20 cm transparent cage with evenly distributed holes (diameter = 1.5 mm) on the side walls. The traps were refitted from inverted 50-mL centrifuge tubes and were placed along the diagonal of the cage. The top of each trap was pierced with a 1-mL pipette tip, which was shortened to ensure flies could access the trap from the pipette. For the olfactory preference assay with mango, one trap was loaded with 60 mg mango flesh and the other trap with 20 μL MO in the cap of a 200-μL PCR tube. For the olfactory preference assay with 1-octen-3-ol (≥98%, sigma, USA), one trap was loaded with 20 μL 10% (v/v) 1-octen-3-ol diluted in MO, and the other with 20 μL MO. A cotton ball soaked in water was placed at the center of the cage to provide water for the flies. Groups of 30 female flies were introduced into the cage for each experiment, and each experiment was repeated to provide eight biological replicates. All experiments commenced at 10 am to ensure circadian consistency. The number of flies in each trap was counted every 2 h for 24 h. We compared the preferences of 3-day-old immature females, 15-day-old virgin females, and 15-day-old mated females. The olfactory preference index was calculated using the following formula41: (number of flies in mango/odorant trap – number of flies in control trap)/total number of flies.Oviposition behavior was monitored in a 10 × 10 × 10 cm transparent cage with evenly distributed holes on the side walls as above. A 9-cm Petri dish filled with 1% agar was served as an oviposition substrate, and the mango flesh, 10% (v/v) 1-octen-3-ol or MO were added at opposite edges of the dish. We tested the preference of flies for different substrates: (1) ~60 mg of mango flesh on one edge and 20 μL of MO on the other; (2) 20 μL of 1-octen-3-ol on one edge and 20 μL of MO on the other; (3) ~60 mg mango flesh on one edge and 20 μL of 1-octen-3-ol on the other; and (4) ~60 mg mango flesh plus 20 μL 1-octen-3-ol on one side and ~60 mg of mango flesh plus 20 μL MO on the other. The agar disc was covered in a pierced plastic wrap to mimic fruit skin, encouraging flies extend their ovipositor into the plastic wrap to lay eggs. The agar disc was placed at the center of the cage, and we introduced eight 15-day-old gravid females. Two Sony FDR-AX40 cameras recorded the behavior of the flies for 24 h, one fixed above the cage to record the tracks and the other placed in front of the cage to record the oviposition behavior. Based on the results from double traps luring assays, a 3 h duration (6–9 h) of the videos was selected to analyze the tracks and spent time of all flies in observed area (the surface of Petri dish). The videos were analyzed using EthoVision XT v16 (Noldus Information Technology) to determine the total time of all flies spent on each side in seconds and the total distance of movement in centimeters, and the tracks were visualized in the form of heat maps17. The number of eggs laid by the eight flies in each experiment was counted under a CNOPTEC stereomicroscope, and each experimental group comprised 7–16 replicates.Annotation of B. dorsalis OR genesD. melanogaster amino acid sequences downloaded from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) were used as BLASTP queries against the B. dorsalis amino acid database with an identity cut-off of 30%. The candidate OR genes were compared with deep transcriptome data from B. dorsalis antennae42, maxillary palps and proboscis, and other tissues.Cloning of candidate B. dorsalis OR genesHigh-fidelity PrimerSTAR Max DNA polymerase (TaKaRa, Dalian, China) was used to amplify the full open reading frame of BdorOR genes by nested PCR using primers (Supplementary Table 2) designed according to B. dorsalis genome data. Each 25-μL reaction comprised 12.5 μL 2 × PrimerSTAR Max Premix (TaKaRa), 10.5 μL ultrapure water, 1 μL of each primer (10 μM), and 1 μL of the cDNA template. An initial denaturation step at 98 °C for 2 min was followed by 35 cycles of 10 s at 98 °C, 15 s at 55 °C and 90 s at 72 °C, and a final extension step of 10 min at 72 °C. Purified PCR products were transferred to the vector pGEM-T Easy (Promega, Madison, WI) for sequencing (BGI, Beijing, China).Transcriptional profilingTotal RNA was extracted from (i) male and female antennae, maxillary palps, head cuticle (without antenna, maxillary palps, and proboscis), proboscis, legs, wings and ovipositors, and (ii) from the heads of 15-day-old virgin and mated females using TRIzol reagent (Invitrogen, Carlsbad, CA). Genomic DNA was eliminated with RNase-free DNase I (Promega) and first-strand cDNA was synthesized from 1 µg total RNA using the PrimeScript RT reagent kit (TaKaRa). Standard curves were used to evaluate primer efficiency (Supplementary Table 3) with fivefold serial dilutions of cDNA. Quantitative real-time PCR (qRT-PCR) was carried out using a CFX Connect Real-Time System (Bio-Rad, Hercules, CA) in a total reaction volume of 10 µL containing 5 μL SYBR Supermix (Novoprotein, Shanghai, China), 3.9 μL nuclease-free water, 0.5 μL cDNA (~200 ng/μL) and 0.3 μL of the forward and reverse primers (10 μM). We used α-tubulin (GenBank: GU269902) and ribosomal protein S3 (GenBank: XM_011212815) as internal reference genes. Four biological replicates were prepared for each experiment. Relative expression levels were determined using the 2−∆∆Ct method43, and data were analyzed using SPSS v20.0 (IBM).Two-electrode voltage clamp electrophysiological recordingsVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pT7Ts for expression in oocytes. The plasmids were linearized for the synthesis of cRNAs using the mMESSAGE mMACHINE T7 Kit (Invitrogen, Lithuania). The purified cRNA was diluted to 2 µg/µL and ~60 ng cRNA was injected into X. laevis oocytes. The oocytes were pre-treated with 1.5 mg/mL collagenase I (GIBCO, Carlsbad, CA) in washing buffer (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, pH 7.6) for 30–40 min at room temperature before injection. After incubation for 2 days at 18 °C in Ringer’s solution (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, 0.8 mM CaCl2), the oocytes were exposed to different concentrations of 1-octen-3-ol diluted in Ringer’s solution from a 1 M stock in DMSO. Odorant-induced whole-cell inward currents were recorded from injected oocytes using a two-electrode voltage clamp and an OC-725C amplifier (Warner Instruments, Hamden, CT) at a holding potential of –80 mV. The signal was processed using a low-pass filter at 50 Hz and digitized at 1 kHz. Oocytes injected with nuclease-free water served as a negative control. Data were acquired using a Digidata 1550 A device (Warner Instruments, Hamden, CT) and analyzed using pCLAMP10.5 software (Axon Instruments Inc., Union City, CA).Calcium imaging assayVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pcDNA3.1(+) along with an mCherry tag that confers red fluorescence to confirm transfection. High-quality plasmid DNA was prepared using the Qiagen plasmid MIDIprep kit (QIAgen, Düsseldorf, Germany). The B. dorsalis OR and BdorOrco plasmids were co-transfected into HEK 293 cell using TransIT-LT1 transfection reagent (Mirus Bio LLC, Japan) in 96-well plates. The fluorescent dye Fluo-4 AM (Invitrogen) was prepared as a 1 mM stock in DMSO and diluted to 2.5 μM in Hanks’ balanced salt solution (HBSS, Invitrogen, Lithuania) to serve as a calcium indicator. The cell culture medium was removed 24–30 h after transfection and cells were rinsed three times with HBSS before adding Fluo 4-AM and incubating the cells for 1 h in the dark. After three rinses in HBSS, 99 μL of fresh HBSS was added to each well before testing in the dark with 1 μL of diluted 1-octen-3-ol. Fluorescent images were acquired on a laser scanning confocal microscope (Zeiss, Germany). Fluo 4-AM was excited at 488 nm and mCherry at 555 nm. The relative change in fluorescence (ΔF/F0) was used to represent the change in Ca2+, where F0 is the baseline fluorescence and ΔF is the difference between the peak fluorescence induced by 1-octen-3-ol stimulation and the baseline. The healthy and successfully transfected cells (red when excited at 555 nm) were used for analysis. The final concentration of 10−4 M was initially used to screen corresponding ORs, and then to determine the response of screened ORs to stimulation with different concentrations of 1-octen-3-ol. Each concentration of 1-octen-3-ol was tested in triplicate. Concentration–response curves were prepared using GraphPad Prism v8.0 (GraphPad Software).Genome editingThe exon sequences of BdorOR7a-6 and BdorOR13a were predicted using the high-quality B. dorsalis genome assembly. Each gRNA sequence was 20 nucleotides in length plus NGG as the protospacer adjacent motif (PAM). The potential for off-target mutations was evaluated by using CasOT to screen the B. dorsalis genome sequence. Each gRNA was synthesized using the GeneArt Precision gRNA Synthesis Kit (Invitrogen) and purified using the GeneArt gRNA Clean-up Kit (Invitrogen). Embryos were microinjected as previously described20. Purified gRNA and Cas9 protein from the GeneArt Platinum Cas9 Nuclease Kit (Invitrogen) were mixed and diluted to final concentrations of 600 and 500 ng/µL, respectively. Fresh eggs (laid within 20 min) were collected and exposed to 1% sodium hypochlorite for 90 s to soften the chorion. The eggs were fixed on glass slides and injected with the mix of gRNA and Cas9 protein at the posterior pole using an IM-300 device (Narishige, Tokyo, Japan) and needles prepared using a Model P-97 micropipette puller (Sutter Instrument Co, Novato, CA). Eggs were injected with nuclease-free water as a negative control. Injection was completed within 2 h. The injected embryos were cultured in a 27 °C incubator and mortality was recorded during subsequent development.G0 mutants were screened as previously described20. G0 adult survivors were individually backcrossed to WT flies (single pair) to collect G1 offspring. Genomic DNA was extracted from G0 individuals after oviposition using the DNeasy Blood & Tissue Kit (Qiagen). The region surrounding each gRNA target was amplified by PCR using the extracted DNA as a template, the specific primers listed in Supplementary Table 2, and 2 × Taq PCR MasterMix (Biomed, Beijing, China). PCR products were analyzed by capillary electrophoresis using the QIAxcel DNA High Resolution Kit (Qiagen). PCR products differing from the WT alleles were purified and transferred to the vector pGEM-T Easy for sequencing. To confirm the mutation was inherited, genomic DNA was also extracted from one mesothoracic leg of G1 flies using InstaGene Matrix (Bio-Rad, Hercules, CA) and was analyzed as above. To avoid potential off-target mutations, heterozygous G1 mutants were backcrossed to WT flies more than 10 generations before self-crossing to generate homozygous mutant flies.Electroantennogram (EAG) recordingThe antennal responses of 15-day-old B. dorsalis adults to 1-octen-3-ol were determined by EAG recording (Syntech, the Netherlands) as previously reported20. Briefly, antennae were fixed to two electrodes using Spectra 360 electrode gel (Parker, Fairfield, NJ, USA). The signal response was amplified using an IDAC4 device and collected using EAG-2000 software (Syntech). Before each experiment, 1-octen-3-ol and other three volatiles (ethyl tiglate, ethyl acetate, ethyl butyrate) were diluted to 10%, 1% and 0.1% (v/v) with MO to serve as the electrophysiological stimulus, and MO was used as a negative control. A constant air flow (100 mL/min) was produced using a controller (Syntech) to stimulate the antenna. We then placed 10 µL of each dilution or MO onto a piece of filter paper (5 × 1 cm), and the negative control (MO) was applied before and after the diluted odorants to calibrate the response signal. The EAG responses at each concentration were recorded for 15–20 antennae, and each concentration was recorded twice. Each test lasted 1 s, and the interval between tests was 30 s. EAG response data from WT and mutant flies for the diluted odorants were analyzed using Student’s t test with SPSS v20.0.Molecular docking and site-directed mutagenesisThe three dimensional-structures of BdorOR7a-6 and BdorOR13a were modeled using AlphaFold 2.044. The quality and rationality of each protein structure was evaluated online using a PROCHECK Ramachandran plot in SAVES 6.0 (https://saves.mbi.ucla.edu/). AutoDock Vina 1.1.2 was used for docking analysis, and the receptor protein structure and ligand molecular structure were pre-treated using AutoDock 4.2.6. The docking parameters were set according to the protein structure and active sites, and the optimal docking model was selected based on affinity (kcal/mol). Docking models were imported into Pymol and Discovery Studio 2016 Client for analysis and image processing. Based on the molecular docking data, three residues (Asn86 in OR7a-6, Asp320, and Lys323 in OR13a) were replaced with alanine by site-directed mutagenesis45 using the primers listed in Supplementary Table 2. Calcium imaging assays and molecular docking of mutated proteins were then carried out as described above.Statistics reproducibilityAll of the olfactory preference assays, oviposition bioassays, expression profiles analysis, EAG recording assays were analyzed using Student’s t-test (*p  More

The pesticide hexachlorocyclohexane (HCH) is a toxic and persistent contaminant in the environment. Some bacteria and fungi can degrade HCH and its isomers under laboratory conditions. However, in heterogeneous environments, where many different factors are at play, the biodegradation capacity is challenged by the availability of nutrients to support degraders’ growth. As opposed to bacteria, fungi are more adapted to heterogeneous habitats, and in some cases mycelial fungi can facilitate the transport of organic substrates throughout the mycosphere, increasing their availability to promote bacterial contaminant biodegradation. However, how this occurs is not entirely understood. In this study, Khan et al. demonstrate that mycelial nutrients transferred from nutrient-rich to nutrient-deprived habitats promote co-metabolic degradation of HCH by bacteria. The authors incubated a non-HCH-degrading fungus (Fusarium equiseti K3) and a co-metabolically HCH-degrading bacterium (Sphingobium sp. S8) in a structured model ecosystem. Results from 13C isotope labelling and metaproteomics showed that fungal 13C was incorporated into bacterial proteins responsible for HCH degradation, thus illustrating the importance of synergistic fungal–bacterial interactions for contaminant biodegradation in nutrient-poor environments. More

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