Philippa Kaur

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The microbiomes associated with insects are important in mediating host health and fitness. In recent years, numerous studies have explored the microbial diversity and variations across different developmental stages in insects, particularly for pests, including Bactrocera dorsalis16, Monochamus alternatus17, and Zeugodacus tau18. Previously, bacterial communities were investigated using inefficient, low-throughput culture-based or conventional molecular methods19,20, inevitably underestimating the microbial abundance. The advancements in sequencing technology have inspired more research on insect microbial communities, thereby enriching the information on the microbiome of insects. However, a comprehensive understanding of the C. megacephala pupal stage microbiome remains unclear. Therefore, this paper presents a study of the diversity and dynamics of bacteria in the pupal stage of C. megacephala using third-generation sequencing of bacterial 16S rRNA. The results provide a better understanding of the C. megacephala microbiome.This annotation results demonstrate that the bacteria in the pupal stage of C. megacephala are rich and diverse, but the diversity is indiscrete. At the phylum level, Proteobacteria, Firmicutes, and Bacteroidetes were the three predominant phyla, similar to the observation from the housefly Musca domestica21, possibly owing to a semblable ecological niche. The bacterial community analysis identified Clostridia and Gammaproteobacteria as the two predominant bacterial classes in the pupal stage of C. megacephala with ~ 30% relative abundances. However, another study of the gut bacteria across the lifecycle of C. megacephala showed Gammaproteobacteria as the dominant class with over 60% relative abundance. These results suggest that Clostridia may be from other C. megacephala tissues apart from the gut.Compared with the previous results about C. megacephala bacterial communities that were determined using culture-based or conventional molecular methods, the microbial diversity was much higher in this study using third-generation sequencing technology22. However, we cannot identify some bacteria to the species level, such as Klebsiella pneumoniae and Aeromonas hydrophila23, so culture-based and conventional molecular methods are also important.Ignatzschineria indica and Wolbachia endosymbiont were the two predominant species in the bacterial communities in the C. megacephala pupal stage. Ignatzschineria indica is a Gram-negative bacterium commonly associated with maggot infestation and myiasis, a probable marker for myiasis diagnosis24,25. Wolbachia are intracellular symbiotic bacteria widely distributed in the reproductive tissues of arthropods. They cause reproductive alterations in their hosts, such as cytoplasmic incompatibility (CI)26, feminization27, killing males28, and inducing parthenogenesis (PI)29. Wolbachia increases the resistance to arbovirus infection, resulting in decreased virus transmission. The reproductive regulation of Wolbachia on target organisms may be important in future biological prevention and pest control. Since Wolbachia causes CI, Wolbachia-infected populations can be established and released to reduce to the environment to reduce the reproductive potential of harmful target insect populations. Modified Wolbachia that harbor anti-parasitic or anti-viral genes can be adopted to control virus transmission in insects carrying viruses30.However, few studies have reported that Ignatzschineria and Wolbachia can coexist in an individual insect, despite their status as common bacterial genera. Several possibilities may explain this analytical discrepancy. Firstly, in this study, Spearman’s rank correlation between Wolbachia and Ignatzschineria showed a negative correlation, suggesting a competitive relationship between Wolbachia and Ignatzschineria. Secondly, the previous investigations of bacterial communities applied inefficient, low throughput culture-based or conventional molecular methods, potentially generating incomplete results. Finally, numerous studies have established that microbial communities differ between insect populations because of different sampling techniques and procedures31. This study analyzed C. megacephala sampled from a laboratory population reared with pork for five years. Nevertheless, the significant decrease in the relative abundance of Wolbachia observed at the end of the pupal development is unsolved, thus, required further studies.Traditionally, the most common method for pest control is by chemical pesticides. However, the excessive use of chemical pesticides causes the rapid build-up of pesticide resistance and environmental pollution. Therefore, it is urgent to develop biological control methods for pests. Nasonia vitripennis (Walker), is an important parasitoid whose female wasp stings, injects venom, and lays eggs in different fly pupae, where parasitoid eggs, larvae, pupae, and early-stage adults develop. N. vitripennis lives in species of the family Calliphoridae, Sarcophagidae, and Muscidae, where their larvae feed on fly pupae, allowing N. vitripennis to function as a biological agent to control the flies.The microbial communities of fly species and N. vitripennis live in an enclosed environment, providing more opportunities for the N. vitripennis-fly communication. Therefore, the impacts of micro-communities of the fly hosts on N. vitripennis are worth studying, precisely at the pupal stage. Studies of different fly hosts and their corresponding N. vitripennis showed diverse core microbiota, and so other fly hosts shaped the bacterial diversity of their parasitic wasps32. In addition, parasitic wasps infected with Wolbachia produced more female offspring than uninfected ones, further emphasizing the need to improve biological prevention and control efficiency33. Therefore, a deliberate focus to study the micro-communities of different fly species at the pupal stage and the interaction between the fly species and N. vitripennis will guide the development and utilization of N. vitripennis as biological agents for the prevention and control of flies.Approximately half of the bacteria identified at the species level in this study are pathogens or conditional pathogens (Supplementary Table S2), Escherichia coli, Providencia burhodogranariea, and Morganella morganii, among others. Another uncommon pathogenic bacterium, Erysipelothrix rhusiopathiae was also identified at the species level. E. rhusiopathiae is the etiological agent of swine erysipelas and causes economically important chicken, duck, and sheep diseases. Although E. rhusiopathiae primarily infects pigs, it also infects various domestic and wild mammals, including marine mammals, birds, and humans. Humans infected with E. rhusiopathiae develop large areas of red spots on their body. Severe E. rhusiopathiae infection causes endocarditis and septicemia, which have a 38% mortality rate34.However, very few studies have focused on the insects that transmit E. rhusiopathiae35. Considering that the C. megacephala samples in this study were obtained from a laboratory population reared for five years, it is likely that the E. rhusiopathiae originated from infected pork and were transmitted to C. megacephala through feeding. Thus, disease-vector insects can infect and spread pathogens beyond their feeding activities, and disease-vector insects require more comprehensive prevention and control methods (“Supplementary information”).In conclusion, this study comprehensively investigated the pupal stage microbiome of C. megacephala using third-generation sequencing to deepen the understanding of C. megacephala microbial communities on the whole. The study provides a basis for subsequent studies of biological control and the comprehensive utilization of C. megacephala. Future studies should focus on the transmission patterns and biological functions of these microbial species. More

Model frameworkThe capture-recapture model applied here is the hierarchical model for stratified populations proposed by Royle et al.48. The model aims to estimate local population size or community structure49 using capture-recapture data from multiple independent locations. In the following, we briefly describe the model in our context, including addressing heterogeneity in detection probability.Let us consider that we establish S independent camera stations in a survey area. Then, we install K camera traps at each station to monitor exactly the same focal area (totally S × K camera traps will be used). We assume that these camera traps detect animals within the focal areas NT times in total. For animal pass i (i = 1, 2, 3, …, NT), we will obtain (1) at the station where the animal is detected (hereafter station identity; gi), and (2) how many of the K cameras at the station were successful in detecting the animal pass (hereafter detection history; yi). The hierarchal capture-recapture model uses these two data, gi and yi.Let the number of the animal passes at station s be Ns (s = 1, 2, 3, …, S). Then, we assume that Ns follows a Poisson distribution with a parameter λ. In this case, the probability of passage i occurring at station s is expected to be (frac{lambda }{lambda times S}). Thus, station identity, gi, can be modelled as follows:$$g_{i} sim {text{ Categorical}}; left(frac{lambda }{lambda times S}right)$$
When the number of the animal passes at station s, Ns, may have larger variation than expected from the Poisson case, we may assume a negative binomial distribution model or may give a random effect to the parameter of the Poisson distribution at the camera station level.The detection history Y with elements yi can be modelled using a data augmentation procedure47. Specifically, the original detection Y is artificially augmented by many M – n passes with all-zero histories (i.e. not detected by any camera). The augmented data W with elements wi (y1, y2…yNT, 0, 0, … 0) will consist of the passage that occurred but was not detected by any camera (false zero), which occurs with probability ψ, and the passage that did not occur (structural zeros) with the probability 1 − ψ. A set of latent augmentation binary variables, z1, z2, … zM, is introduced, which denotes the false zero (z = 1) and the structural zero (z = 0). That is$$z_{i} sim {text{ Bernoulli }}left( psi right).$$The elements of the augmented data, wi, can be modelled conditional on the latent variables zi. There would be two alternative approaches to modelling the wi.The simplest one may regard wi as random binomial variables. That is$$w_{i} |z_{i} = , 1sim {text{ Binomial }}left( {K,p} right)$$When accounting for the heterogeneity of detection among animal passes, it can be accommodated using a beta distribution as follows;$$w_{i} |z_{i} = , 1sim {text{ Binomial }}left( {K,p_{i} } right)$$$$p_{i} sim {text{ Beta}}left( {alpha ,beta } right)$$The expected detection probability can be derived from (widehat{alpha }/(widehat{alpha }+widehat{beta })) and the correlation coefficients can be calculated by (1/(widehat{alpha }+widehat{beta }+1)).Alternatively, we can regard wi as a categorical variable that takes values from zero to K.$$w_{i} sim {text{ Categorical }}left( pi right)$$
where π is a probability vector of length K + 1. For simplicity, let us consider two camera traps installed at each station, and those cameras have equal detection probability. Then, wi can take either 0 (i.e. zi = 0 or both camera traps missed animals with conditional on zi = 1), 1 (i.e. only one camera trap detected animals with conditional on zi = 1), or 2 (i.e. both camera traps detected animals with conditional on zi = 1). Thus, when we define the probability that wi takes 0, 1, 2 with conditional on zi = 1, as φm (m = 1, 2, 3), the elements of π is equal to {zi × φ0 + (1 − zi)}, {zi × φ1}, {zi × φ2}, respectively.We then take different modelling approaches depending on whether detection probability among animal passes is heterogeneous or not. When two camera traps at a station detect animals independently with the same probability ρ, φ0, φ1, and φ2 can be expressed as a function of ρ, i.e. (1 − ρ)2, 2 × ρ × (1 − ρ)2, ρ2, respectively (Clare et al.47). On the other hand, when detections by the two camera traps are correlated, we need to estimate three real parameters φm that designate the probabilities of all outcomes wi|zi = 1. We assume that ρm follows the Dirichlet distribution with the parameter γm (m = 1, 2, 3). That is$$varphi_{m} sim {text{ Dirichlet}}left( {gamma_{1} ,gamma_{2} , , gamma_{3} } right)$$In this approach, the expected detection probability can be derived from ({widehat{varphi }}_{1}/2+{widehat{varphi }}_{2}) and the correlation coefficients can be calculated by ({widehat{varphi }}_{2}-{({widehat{varphi }}_{1}/2+{widehat{varphi }}_{2})}^{2}).Compared to the beta-binomial distribution approach, the approach using categorical-Dirichlet distribution might be more flexible in accommodating detection heterogeneity while it might be more challenging to estimate the model parameters. In either approach, the expected total number of animal passes can be expressed as (lambda times S). Thus, ψ can be fixed as follows:$$psi = frac{lambda times S}{M}$$For more details of the models, see Royle et al.48 and Clare et al.44.Testing the effectiveness of the hierarchical capture-recapture modelWe performed Monte Carlo simulations to evaluate the effectiveness of the hierarchical capture-recapture model. Because the model reliability has been confirmed well48, we here focused on the effects of heterogeneity in detection probability on the accuracy and precision of the estimates.We assumed that the number of detections by camera traps followed a negative binomial distribution with a mean of 5.0 and dispersion parameter 1.27, which derived the actual data on an ungulate in African rainforests34. We also assumed two camera traps each at 30 stations (i.e. 60 camera traps in total). We generated detection histories (i.e. the number of camera traps successfully detecting animals in each animal passage) using a beta-binomial distribution with the expected detection probability at 0.8 or 0.4. We varied the correlation coefficients (= 1/(α + β + 1)), from 0.1 to 0.5 in 0.1 increments. The scale parameters of the beta distributions for each scenario are shown in Table 1. Additionally, to determine the effects of sample sizes on the accuracy and precision of estimates, we increased the number of camera stations at 100. Since this setting requires much computation time, we only assumed a detection probability of 0.4 and a correlation coefficient of 0.3.We estimated the parameters of the hierarchical capture-recapture models assuming a beta-binomial distribution and a categorical-Dirichlet distribution using the Markov chain Monte Carlo (MCMC) implemented in JAGS (version 3.4.0) in all the simulations. We assumed that the number of animal passes followed a negative binomial distribution. For the model assuming a beta-binomial distribution, we transformed the scale parameters, α and β as p*phi and p*(1 − phi), respectively (p is an expected detection probability). Then we used a weakly informative prior (gamma distribution with shape = 10 and rate = 2) for phi and a non-informative uniform distribution from 0 to 1 for the detection probability49. For the model assuming a categorical-Dirichlet distribution, the Dirichlet prior distribution was induced by treating each γm ~ Gamma(1, 1) and calculating each probability by ({varphi }_{m}={{gamma }_{m}}/{sum }_{m=1}^{M}{gamma }_{m}) followingv and Clare et al.44. We generated three chains of 3000 iterations after a burn-in of 1000 and thinned by 5. The convergence of models was determined using the Gelman–Rubin statistic, where values  More

Timeseries imaging tracks gene expression in spatial systemsRecent studies have shown it possible to identify the members of microbial consortia as well as their gene expression within spatially-structured systems30,33,34. However, these methods capture data cross-sectionally and are unable to provide temporal insight into gene expression patterning as it emerges in these cell populations. To bridge this gap, we built a fluorescent imager inside an incubator (Supplementary Fig. 1). Our framework characterizes cellular growth and gene expression in spatially-structured environments with previously unattainable time-resolution and throughput. Fluorescently labeled cells are illuminated using LEDs connected to a custom-built control system (see methods). The images are background corrected and analyzed, tracking colony growth and gene expression information (Supplementary Figs. 2, 3) straight from the spatially-structured system.In our experiments, we utilized a dual-labeled P. aeruginosa PA14 strain harboring PBad-DsRed(EC2)35 driven by L-arabinose in the plate media, which cannot be metabolized by the cells36, and PrhlAB-GFP28,37. When grown in spatial structure, the constitutive expression of DsRed provided a measure of the local density of bacteria (Supplementary Fig. 4). In all our experiments, the dynamical expression of GFP, validated by RT-qPCR (Supplementary Fig. 5) (see methods), reported on the expression of rhlAB.Using these data, we were able to characterize how the surroundings experienced by these microbes influence the dynamics of their cooperative behavior directly in a spatially-structured setting.Rhamnolipid production differs in liquid and spatial environmentsRhamnolipids are necessary for cooperative swarming behavior in P. aeruginosa and for other traits related to virulence26. Rhamnolipids can be produced in liquid culture10,20,28,38, thus rhamnolipid production is often studied in detail there. Despite recent work indicating that gene expression related to quorum signaling systems in P. aeruginosa may differ in spatial structure29, no studies assess how downstream genes, such as rhlAB, may be affected in spatially-structured colonies. Given the relevance of these diffusible inputs to the rhlAB system, we hypothesized that there could be differences between gene expression patterns in liquid and spatial environments.We compared P. aeruginosa biomass growth and gene expression in the liquid and spatial environments (Fig. 1a). Liquid culture data was collected following prior methods28. To interrogate the spatial system, we used the protocol from the classic Colony Forming Unit (CFU) assay. Cells were seeded with extreme dilution and we observed the behavior of the resultant colonies (cCFUs) across time and within the random configurations generated.Fig. 1: Rhamnolipid production differs between liquid culture and surface-attached P. aeruginosa.a Cartoon depictions of liquid and spatially-structured environments used in this study. b Optical density timeseries describing P. aeruginosa growth in liquid culture. [Blue] Biomass growth without exogenous quorum signals. [Purple] Biomass growth with exogenous quorum signals. c DsRed fluorescent timeseries generated from a custom-built imager (Supplementary Fig. 1) and custom software (Supplementary Fig. 3) describing P. aeruginosa growth in colony forming units (CFU). [Blue] Biomass growth without exogenous quorum signals [Purple] Biomass growth with exogenous quorum signals added to the plate media. [Inset] Example plate showing colonies at 48 h. Scale bar 1 cm. d Promoter activity (left[frac{{dGFP}}{{dt}}cdot frac{1}{{{OD}}_{600}}right]) of PrhlAB with respect to culture growth rate (left[frac{d{{OD}}_{600}}{{dt}}cdot frac{1}{{{OD}}_{600}}right]). [Blue] without exogenous quorum signals [Purple] with exogenous quorum signals. e Promoter activity (left[frac{{dGFP}}{{dt}}cdot frac{1}{{DsRed}}right]) of PrhlAB with respect to CFU growth rate (left[frac{{dDsRed}}{{dt}}cdot frac{1}{{DsRed}}right]). [Blue] without exogenous quorum signals [Purple] with exogenous quorum signals provided in the plate media.Full size imageWe observed differences in growth between cells grown in liquid culture (Fig. 1b) and spatial structure (Fig. 1c) with the same media composition. The growth pattern observed in liquid culture recapitulates previously reported data22,28. In comparing WT growth (dark blue data in Fig. 1b, c) between environments, we observed that both achieve a period of exponential growth, followed by a period of slowed growth. This sub-exponential growth is prolonged and no period of biomass decay is observed in the spatially-structured environment during our observation window.Quorum signal perturbation has long been an experimental tool to determine if a phenotype is responsive to social signaling9,10. rhlAB gene expression in particular is known to be downstream of both the las and rhl quorum signal systems39,40. However, it has previously been shown that liquid culture perturbation with additional C4-HSL and 3-oxo-C12-HSL, the rhl and las quorum signal system auto-inducers respectively, do not illicit significant change in growth or PrhlAB dynamics in this strain of P. aeruginosa22. We replicated this liquid culture result (Fig. 1b, purple data). In the spatially-structured system, we performed this perturbation by including both quorum signal molecules in the plate media in the same concentration by volume as previously published22. This analysis was done using biological replicates with More

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This research focuses on the quantitative and qualitative analyses of cyanobacteria and microalgae present in rainfall during the summer phytoplankton bloom season of August–September 2019. In addition, a continuous episode of rainfall over several days was selected to demonstrate the washout process of microorganisms from the air with rain.Quantity of cyanobacteria and microalgae washed out with rain during the growing seasonCurrently, there is a growing number of scientific articles on cyanobacteria and microalgae in the atmosphere8. Unfortunately, there is a reference methodology for efficiently counting the microorganisms present in the air or in rainfall. A popular method for quantifying cyanobacteria and microalgae in the air is to show the number of taxa found in the collected samples after growth6,31,42,43,44,45,46. In this study, a total of 16 taxa of airborne cyanobacteria and microalgae were found in the samples. In the rainwater samples obtained during the summer of 2019, 11 taxa of cyanobacteria and microalgae were distinguished. The green algae in the rainwater samples included Bracteacoccus sp., Oocystis sp., Coenochloris sp., Chlorella sp., and Chlorococcum sp., while the cyanobacteria included Leptolyngbya sp., Pseudanabaena sp., Synechococcus sp., and Synechocystis sp. In addition, Chrysochromulina sp., which belongs to Haptophyta, was observed.Other studies recorded the presence of several to several dozen taxa in the air6,31,42,43,44,45,46. Certainly, a number of factors, starting with atmospheric conditions and ending with physical and chemical parameters of the surrounding waters, influence the diversity of cyanobacteria and microalgae in the atmospheric air. Analyzing global trends, only cyanobacteria have been found in the atmosphere of every region of the world31. However, according to Dillon et al.47, cyanobacteria have been detected in clouds at variable abundances between ~ 1% and 50% of the total microbial community. Xu et al.48 found that cyanobacteria constituted only 1.1% of the total bacterial community in clouds. It needs to be highlighted that there is still a lack of research available to provide this type of information for rainfall samples.For the period from July to September 2019, the results showed that the number of cyanobacteria and microalgae cells present in rainfall varied over time (Fig. 1) and ranged between 100 cells L–1 and 342.2 × 103 cells L–1. From July to the end of August, the cell number was relatively low, ranging from 100 cells L–1 to 28.6 × 103 cells L–1. This variability was related to the change in the biomass of blue green algae in the Gulf of Gdańsk (Table S2; Fig. 1). Therefore, this research also shows the close relationship between the processes taking place in the Baltic Sea and the presence of cyanobacteria and microalgae in the atmosphere. As the biomass of cyanobacteria in the Baltic Sea increased, the number of cyanobacteria and microalgae cells in the rainfall samples also increased (***p  More

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