Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M

doi:10.21203/rs.3.rs-4321283/v1

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Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M

https://doi.org/10.21203/rs.3.rs-4321283/v1

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Background

Microbial communities including symbionts play vital roles in insect hosts. Abiotic factors, especially ecological factors also have significant influence on the structure of the microbiome and the abundance of symbionts within hosts. However, the effects of the bacterial symbionts and ecological factors on the microbiota in host whitefly remains poorly understood.

Results

In this study, 49 Bemisia tabaci MED populations collected in 23 locations around the world were sequenced using 2bRAD-M, to explore the relationships among ecological factors, symbionts and microbial diversities in whiteflies. Results revealed that microbial community structures significantly differed in the different geographical B. tabaci MED populations, and the abundance of many symbionts including Portiera, Hamiltonella, Rickettsia, Cardinium, and Wolbachia, significantly influenced with one another. Also, the diversity of bacterial communities in whiteflies were significantly affected by the relative abundance of symbionts including Cardinium and Hamiltonella. Meanwhile, environmental factors including temperature, precipitation, longitude and latitude significantly influenced the abundance of many symbionts and the diversity of bacterial communities in B. tabaci MED.

Conclusions

Overall, our results revealed complex interactions among ecological factors, among ecological factors, microbiota diversity and symbionts in B. tabaci MED. This helps to comprehend the complex interactions among these factors in insect hosts.

symbiont

whitefly

2bRAD-M sequencing

microbial diversity

ecological factors

Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is notorious for its destruction to host plants and extremely rapid invasive capacity which has enabled its colonization in almost every part of the world including temperate, sub-tropical and tropical locations [1,2]. It is a pecies complex, with at least 44 cryptic species identified up to date, which have a wide range of host plants [3,4,5]. More importantly, B. tabaci is the main insect vector of many begomoviruses which cause enormous damages to field crops [6,7]. B. tabaci MED is considered one of the most invasive species and widespread globally including in countries such as China, USA, and many Mediterranean countries [3,8,9]. Especially in China, the B. tabaci MED has replaced B. tabaci MEAM1 to be the dominant cryptic species in the past few years [10,11].

Bacterial communities have vital influence on the development, health, and biological processes of insect hosts. For example, many bacteria (especially endosymbionts) provide hosts with nutrients including vitamins and essential amino acids, as well as protect their hosts from adverse ecological factors including heat shock, predators and pesticides [9,12,13,14,15,16].

Biotic and abiotic factors such as diet, shape the structure of bacterial communities in insects [17,18,19,20,21,22,23,24]. Also, high temperatures significantly influence the diversity and abundance of symbionts in whiteflies [24] and host genotype and Wolbachia infection affect the densities of other Wolbachia strains [21]. Further, the temporal and spatial distributions of hosts influence the bacterial communities within them [25].

Endosymbionts are widespread in insects and always have vital influence on hosts. They are vertically heritable bacteria which mostly reside in the bacteriocytes of insect hosts, especially primary endosymbionts [26,27,28,29]. In B. tabaci, there is one primary endosymbionts Candidatus Portiera aleyrodidarum and 6 other secondary endosymbionts, which include Rickettsia, Cardinium, Fritschea, Wolbachia, Hamiltonella and Arsenophonus [4,9,30,31]. They are both important for host whiteflies. For instance, Portiera and Hamiltonella cooperate to provide host B. tabaci MED with essential nutrition including co-factors and vitamins [32]. Cardinium infection significantly affects the fitness, fecundity, thermotolerance and microRNA expression in host B. tabaci [9,33,34,35,36]. Rickettsia also enhances the thermotolerance [37] and provisions of host whitefly with nutrition and resistance [38], as well as alter the defense pattern of host plants [39]. However, the interactions among different symbionts in B. tabaci remain poorly explored.

The presence of endosymbionts in insects can significantly change the structure of bacterial communities in insect hosts [40,41,42]. Also, the existence and abundance of symbionts in insects can be significantly influenced by abiotic factors [43,44,45]. Additonally, the influence of environment on symbionts may contribute to changes in the microbiota structure in host insects [46]. Further, more extreme high temperatures in the future [47,48], will have impacts on insects and their bacterial communities [49]. However, related research in whiteflies are limited, and the relationship among microbial communities and symbionts in whiteflies and ecological factors require clarification.

2bRAD-M is a novel metagenome sequencing method especially for low-biomass or degraded microbiomes, which accurately generate species level taxonomic profiles of samples [50], which has been widely applied in medical and agricultural research [51,52,53;54]. Herein, 2bRAD-M sequencing was applied to 49 B. tabaci MED populations collected from 23 locations spread globally, to reveal the influence of ecological factors as well as the symbionts on the microbiota in B. tabaci. The 3 main purposes were: 1) clarifying the structure and constients of different B. tabaci MED populations from various geographical areas; 2) exploring the relationships among different symbionts and the influence of disparate symbionts on microbiota diversities in whiteflies; 3) illustrating the effects of ecological factors on bacterial communities as well as symbiont abundance in B. tabaci MED. The results showed a significant influence of ecological factors including abiotic factors on microbial communities and strong effects of symbionts on the bacterial diversity in B. tabaci MED.

Global collection of B. tabaci MED populations

We collected 49 populations of B. tabaci MED on different host plants, from 23 locations in eight countries spanning Asia, Europe, and North America, from 2006 to 2018. The whitefly samples were placed in alcohol after collection and transported to the laboratory. Identification of B. tabaci and Cardinium in whiteflies were performed using conventional PCR following previously described methods [9]. Female adults were used in all experiments, with the detailed information provided in Table 1 and Table S1. One replicate means one B. tabaci female adult.

Table 1

Collection information of *Bemisia tabaci* MED populations used in this experiment
Group Name	Population Name	Collected location	Collected year	Host plant	Replicates
Kr	Kr1	Gyeonggi, Korea	2018	cucumber	5
	Kr2	South Chungcheong Province, Korea	2018	cucumber	5
	Kr3	Jeollabuk-do, Korea	2018	sweet pepper	5
	Kr4	Jeollanam-do, Korea	2018	cucumber	5
	Kr5	South Gyeongsang Province, Korea	2018	cucumber	5
	Kr6	North Gyeongsang Province, Korea	2018	muskmelon	5
FJ	FJ	Fuzhou, Fujian Province, China	2018	eggplant	5
JX	JX	Nanchang, Jiangxi Province, China	2018	eggplant	5
AH	AH	Hefei, Anhui Province, China	2018	eggplant	5
JS	JS1	Nanjing, Jiangsu Province, China	2012	eggplant	10
JS	JS2	Nanjing, Jiangsu Province, China	2018	eggplant	5
ZJ	ZJ	Hangzhou, Zhejiang Province, China	2018	gynura bicolor	5
GD	GD	Guangzhou, Guangdong Province, China	2018	eggplant	5
JL	JL	Tonghua, Jilin Province, China	2018	cucumber	5
JN	JN1	Jinan, Shandong Province, China	2008	eggplant	10
	JN2	Jinan, Shandong Province, China	2013	eggplant	10
	JN3	Jinan, Shandong Province, China	2015	eggplant	10
	JN4	Jinan, Shandong Province, China	2017	eggplant	10
HN	HN	Lingshui, Hainan Province, China	2017	tomato	9
BJ	BJ	Beijing, China	2012	eggplant	10
XJ	XJ	Wulumuqi, Xinjiang Province, China	2012	eggplant	10
HuB	HuB	Wuhan, Hubei Province, China	2012	eggplant	10
USA	USA1	Lowes, Tucson, Arizona, USA	2006	poinsettia	10
	USA2	Kmart, Tucson, Arizona, USA	2008	poinsettia	10
	USA3	Trader Joe’s, Tucson, Arizona	2008	poinsettia	10
Group Name	Population Name	Collected location	Collected year	Host plant	Replicates
USA	USA4	Walmart#2, Tucson, Arizona	2008	poinsettia	10
ESP	ESP1	Madrid, Spain	2006	tomato	10
	ESP2	Madrid, Spain	2007	tomato	10
	ESP3	Madrid, Spain			10
CR	CR	Croatia	2007	poinsettia	10
BH	BH	Bosnia and Herzegovina	2007	eggplant	10
CY	CY	Cyprus	2007		10
IL	IL	Israel	2017		10
DZ	DZ1	Dezhou, Shandong Province, China	2008		10
	DZ2	Dezhou, Shandong Province, China	2013		10
	DZ3	Dezhou, Shandong Province, China	2015		10
	DZ4	Dezhou, Shandong Province, China	2017		10
LC	LC1	Liaocheng, Shandong Province, China	2008		10
	LC2	Liaocheng, Shandong Province, China	20013		10
	LC3	Liaocheng, Shandong Province, China	2015		10
	LC4	Liaocheng, Shandong Province, China	2017		10
SG	SG1	Shouguang, Shandong Province, China	2008		10
	SG2	Shouguang, Shandong Province, China	2013		10
	SG3	Shouguang, Shandong Province, China	2015		10
	SG4	Shouguang, Shandong Province, China	2017		10
ZZ	ZZ1	Zaozhuang, Shandong Province, China	2008		10
	ZZ2	Zaozhuang, Shandong Province, China	2013		10
	ZZ3	Zaozhuang, Shandong Province, China	2015		10
	ZZ4	Zaozhuang, Shandong Province, China	2017		10
A replicate means one female adult and all replicates of one population were collected in the same place within 1 hm².

DNA extraction and 2bRAD-M sequencing

2bRAD-M sequencing methods were used to measure the bacterial communities in whiteflies. The 2bRAD-M sequencing and libraries constructions were performed by Qingdao OE Biotech Co., Ltd. (Qingdao, China). The library constructions followed previously published protocols [55] with minor modifications. Briefly, the whitefly DNA (100 ng) of each single sample digestion was performed with 4 U of the enzyme BcgI (NEB) under 37°C for 3 h. After that, the DNA fragments were all ligated with specific adaptors. Then, a mix of 5 µL digested DNA combined with 10 µL of ligation master mix containing 0.2 µM each of two adaptors and 800 U of T4 DNA ligase (NEB) was processed for ligation reaction. The next ligation reaction was performed under 4°C for 12 h.

Subsequently, the ligation products were PCR amplified; then, 8% polyacrylamide gel was used for electrophoresis. Based on the electrophoresis results, an approximately 100-bp band of the gel was excised, and the DNA fragments from the gel were then reconstiuted with DEPC water under 4°C for 12 h. PCR test was conducted with platform-specific barcode-bearing primers to introduce sample-specific barcodes. Each PCR sample was 20 µL, with a 25-ng gel-extracted PCR product, 0.2 µM each of forward and reverse primers, 0.3 mM dNTP mix, 0.4 U Phusion high-fidelity DNA polymerase (NEB), and 1×Phusion HF buffer. After PCR amplification and electrophoresis, QIAquick PCR purification kit (50) (Qiagen) was used to purify the PCR products and the Illumina Nova PE150 platform was used for sequencing.

Data analysis of 2bRAD-M sequencing results

Information from the NCBI database for microbial genomes (including 173,165 species of fungi, bacteria, and archaea) was used for 2bRAD-M analysis. The restriction enzymes of 16 type 2B were used to restrict the sample to fragments with built-in Perl scripts. RefSeq (GCF) number was subsequently used to assign the set of 2bRAD-M tags from all microbial genome information. Moreover, the whole genome’s corresponding GCF taxonomic information was also assigned. After that, every GCF’s 2bRAD tags that only occurred once were selected for comparison with those of all the other 2bRAD-M tags. The 2bRAD-M tags that were unique to a species were considered a species-specific 2bRAD-M marker, and those markers were used to form a reference database. A detection threshold of 0.0001 (0.01%) relative abundance was used as a default [56].

To calculate the relative abundance of each bacterium, the microbial species within each sample were first identified. All sequenced 2bRAD tags after quality control were mapped (using a built-in Perl script) against the 2bRAD marker database which contains all 2bRAD tags theoretically unique to each of 26,163 microbial species in the database. To control the false-positive in the species identification, a G score was derived for each species identified within a sample as below, which is a harmonious mean of read coverage of 2bRAD markers belonging to a species and the number of all possible 2bRAD markers of this species. The threshold of the G score for a false positive discovery of microbial species was set to 5 [51].

$${\text{G} \text{s}\text{c}\text{o}\text{r}\text{e}}_{\text{s}\text{p}\text{e}\text{c}\text{i}\text{e}\text{s} \text{i}}=\sqrt{{S }_{i}\times {t}_{i}}$$

the number of reads assigned to all 2bRAD markers belonging to species i within a sample.

number of all 2bRAD markers of species i that have been sequenced within a sample.

Then, the average read coverage of all 2bRAD markers for each species was calculated, which represent the number of individuals belonging to a species present in a sample at a given sequencing depth. The relative abundance of a given species is then calculated as the ratio of the number of microbial individuals belonging to a species against the total number of individuals from known species that can be detected within a sample.

$${Relative abundance}_{species i}=\frac{\raisebox{1ex}{${S}_{i}$}\!\left/ \!\raisebox{-1ex}{${T}_{i}$}\right.}{{\sum }_{\text{i}=1}^{\text{n}}\raisebox{1ex}{${S}_{i}$}\!\left/ \!\raisebox{-1ex}{${T}_{i}$}\right.}$$

the number of reads assigned to all 2bRAD markers of species i within a sample.

the number of all theoretical 2bRAD markers of species i.

The diversity and abundance indexes, and infection of symboints as influenced by climate or geographical factors were described by two structural equations models (SEM) with Satorra-Bentler correction, respectively. To reduce heteroscedasticity, the log value of “precipitation” was used in SEMs. The SEM models was accepted when the P value > 0.05 and CFI > 0.95 after excluding the redundant paths step by step based on a lower AIC value. To exclude the variance of each parameter, the coefficients of SEM were estimated by the standardized transformation.

The relative abundances of Cardinium and Rickettsia (Fig. 1) and microbial diversities of samples (Fig. 3) were all first tested for normality (Kolmogorov-Smirnov test) and homogeneity of group variances (Levene’s test). When the data followed normal distribution, they were analysed with a one-way ANOVA with post-hoc Tukey HSD analysis. Data which did not follow a normal distribution, were analyzed by a Kruskal-Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons.

To analyze the relationship between symbionts and microbiota diversities, all data of symbiont relative abundance (s) were logit transformed as follows (Figs. 4, 5, 6, 7 and 8):

$$logit\left(s\right)=\text{l}\text{g}(\text{s}+1)$$

When analyzing the relationship between symbionts and microbiota diversities, the diversity indexes were recounted after removing the analyzed symbiont (Fig. 4). The vegan and picante packages in R were used to compute the α-diversity indexes of microbiota in B. tabaci. QIIME software was used to generate the PCoA plots of Bray-Curtis intersample distances and the classification probabilities [57]. Spearman analysis was used for analyzing the relationship symbionts and microbiota diversity indexes. Pearson analysis was used for the relationship between symbiont and ecological factors as well as the relationship between different symbionts. SPSS 21.0 was used to carry out all statistical tests.

Both Arsenophonus and Wolbachia in B. tabaci had too many 0 values, therefore a categorical analysis was used to analyze the relationships between the 2 symbionts and other symbionts as well as ecological factors. All B. tabaci populations were categorized into 2 groups with the Arsenophonus or Wolbachia abundance: one group with Arsenophonus or Wolbachia being present (abundance > 0) and one group with Arsenophonus or Wolbachia being absent (abundance = 0). Mann-Whitney U-test was used to compare the symbiont abundance and diversity indexes between the 2 groups (Fig. S1 and S2). All figures were made by GraphPad Prism 9.0.0. Analysis with generalized linear models was carried out in SPSS 21.0.

Cardinium and Rickettsia abundance in Bemisia tabaci MED collected globally

The 2bRAD-M sequencing results showed that Cardinium infection rate varied significantly among B. tabaci MED collected from the different areas (P < 0.001, Kruskal–Wallis test, Fig. 1 left panel). Whiteflies collected in Israel (IL population) had no Cardinium (no sample’s relative abundance of Cardinium exceeded 0.01%). Whiteflies collected from South Korea (Kr) had a relative higher Cardinium abundance (21.54 ± 3.24%, Mean ± SEM) compared to whiteflies collected in other areas (Fig. 1, left panel). Rickettsia was abundant in B. tabaci MED populations with low Cardinium infection such as IL (68.07 ± 4.57%, Mean ± SEM), CY (69.84 ± 7.14%, Mean ± SEM), ZJ (48.21 ± 13.19%, Mean ± SEM) and FJ (43.76 ± 5.30%, Mean ± SEM) populations, but was lower in Cardinium-abundant populations, such as in Kr population (relative abundance of Rickettsia lower than 0.01%) (Fig. 1, right panel).

Another interesting finding was that the relative abundance of Cardinium was higher in high-latitude populations compared to those in relatively low-latitude populations. In East Asia, high-latitude populations such as JL, XJ and Kr populations (more than 7.38%) had a relatively higher Cardinium abundance than low-latitude populations including FJ, GD, HN populations (lower than 4.16%). In Europe and the Mediterranean region, the relatively higher latitude populations including such as CR population also had higher Cardinium abundance (above 14.16%) than BH, CY and IL populations (lower 1.00%) which were collected in relatively low-latitude areas (Fig. 1, left panel).

Microbial composition of different Bemisia tabaci MED populations

The microbial communities in each B. tabaci MED population, was identified to the species level. One primary symbiont Portiera aleyrodidarum and five secondary symbionts (Hamiltonella, Cardinium, Wolbachia, Rickettsia and Arsenophonus) were discovered in all 49 B. tabaci MED populations. Portiera aleyrodidarum was the most abundant bacteria in almost all geographical populations except for IL and CY populations (in which the abundance of Rickettsia was higher than Portiera), with a relative abundance being above 28.82% (lowest in CY population) in all whitefly populations.

Rickettsia was abundant in the CY population (69.84% of all bacteria) and IL population (68.07%). However, no sequences of Rickettsia were identified in the AH, JX, HuB, XJ and all South Korea populations. The Rickettsia species identified in the samples included Rickettsia sp. Strain MEAM1, Rickettsia hoogstraalii, Rickettsia felis and Rickettsia bellii. Of these, Rickettsia sp. Strain MEAM1 was the dominant species in all Rickettsia-infected samples. Rickettsia bellii showed a lower infection rate in ZZ, JN, HN and USA populations (relative abundance lower than 0.1‰). Likewise, Rickettsia hoogstraalii showed a lower infection rate in SG, HN, JL, CR, CY populations (relative abundance lower than 0.1‰). Rickettsia felis (relative abundance 1.14‰) was identified only in the BH population.

Interestingly, in all populations which had a high Rickettsia abundance, showed lower Cardinium abundance. On the other hand, Cardinium-abundant populations (such as the Korea populations) showed no or less abundant Rickettsia, which imply a potential antagonistic interaction between Cardinium and Rickettsia in B. tabaci MED.

Four Cardinium species were identified in this study (Cardinium cBtQ, Cardinium cEper1, Cardinium endosymbiont of Culicoides punctatus and Cardinium cSfur). Of these, Cardinium cBtQ was the dominant species in all Cardinium-infected samples. Cardinium cEper1 had a lower infection rate (relative abundance lower than 0.1‰), in the JS, Kr2, Kr5, ZZ, LC, AH and JL populations. Likewise, Cardinium cSfur had a lower infection rate in the GD, SG, SPA3 and LC populations, while Cardinium endosymbiont of Culicoides punctatus was only detected in the Kr1 population.

The results showed that all whitefly populations were infected with Hamiltonella defensa, with a relative abundance ranging from 0.02% (lowest in the JX population) to 18.37% (highest in the HuB population).

Seven Wolbachia species were identified from the whitefly samples (Wolbachia endosymbiont of Bemisia tabaci, Wolbachia endosymbiont of Cylisticus convexus, Wolbachia endosymbiont of Diaphorina citri, Wolbachia endosymbiont of Folsomia candida, Wolbachia endosymbiont of Laodelphax striatellus, Wolbachia endosymbiont of Leptopilina clavipes, Wolbachia endosymbiont of Nilaparvata lugens, Wolbachia pipientis wAlbB). Of these, Wolbachia endosymbiont of Bemisia tabaci was the dominant in all Wolbachia-infected samples. However, its abundance was relatively low (below 5.94%) in all samples, except for the JS2 population, which had a relative abundance of 21.12% of all bacterial communities. The relative abundance of the other Wolbachia species was much lower (relative abundance lower than 0.1‰) in all samples. In some East Asia populations including BJ, HN, Kr1, Kr2 and Kr3 populations, no Wolbachia was detected.

Arsenophonus was detected in several populations (including AH, JX, BH, CR, CY and IL populations) with a low-abundance rate below 2.8% (Fig. 2). Four Arsenophonus species was identified from all samples in this study (Arsenophonus endosymbiont of Bemisia tabaci Asia II3, Arsenophonus endosymbiont of Aleurodicus floccissimus, Arsenophonus endosymbiont str Hangzhou of Nilaparvata lugens, Arsenophonus nasoniae). Of these, Arsenophonus endosymbiont of Bemisia tabaci Asia II3 was the dominant in all Arsenophonus-infected samples.

Bacterial diversity in geographically different Bemisia tabaci MED populations

Three α-diversity indexes (including Chao1, Simpson and Shannon indexes) were applied to determine the diversities of the bacterial communities in each whitefly population. The diversity indexes were all analyzed with the Kruskal–Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons (Fig. 3), as none followed a normal distribution. The Shannon and Simpson index analysis showed a similar result for the community diversity index for all whitefly populations. Results showed that the JL population had the highest Simpson index (0.55 ± 0.02, Mean ± SEM) and a relatively high Shannon index (0.97 ± 0.05, Mean ± SEM), while the CR population had the highest Shannon index (1.04 ± 0.12, Mean ± SEM) and a relatively high Simpson index (0.49 ± 0.06, Mean ± SEM). On the other hand, the JX population had the lowest Shannon index (0.21 ± 0.05, Mean ± SEM) and the lowest Simpson index (0.09 ± 0.03, Mean ± SEM) (Fig. 3A and 3B). Results of the Chao1 diversity index showed that the AH population had the highest bacterial community richness (60.40 ± 9.18, Mean ± SEM), while the BJ population had the lowest Chao1 index (17.00 ± 1.91, Mean ± SEM) (Fig. 3C). Interestingly, the α-diversity indexes of Cardinium abundant populations (such as JL, CR, USA and Kr populations) were higher than diversity indexes of Cardinium limited populations (such as JX, BJ, IL and HuB populations) (Fig. 3).

Relationship between symbionts and bacterial diversities in Bemisia tabaci MED

The correlations between the relative abundance of symbionts and the α-diversity indexes (including Shannon, Chao1 and Simpson indexes) of microbial communities in whitefly, were analyzed using Spearman correlation. The microbiota diversity indexes were computed after removing the compared symbiont in this experiment. The results revealed that Cardinium abundance significantly influenced the B. tabaci microbiota Simpson diversity index negatively (r = -0.449, P < 0.01) (Fig. 4B). Hamiltonella had a significant negative effect on both the Shannon diversity (r = -0.498, P < 0.01) and Simpson diversity (r = -0.498, P < 0.01) of host whitefly (Fig. 4G and 4H), while Portiera and Rickettsia did not significantly influence the diversity indexes of bacterial communities in B. tabaci (Fig. 4). Categorical analysis showed that both Wolbachia and Arsenophonus did not significantly affect the diversity of host whitefly (Fig. S1 and S2).

Relationships among various symbionts in Bemisia tabaci MED

Pearson correlation was applied to clarify the complex relationships among the different symbionts in B. tabaci MED. Results showed that the relative abundance of Hamiltonella significantly negatively correlated with the relative abundance of Rickettsia (r = -0.403, P < 0.01) and Wolbachia (r = -0.351, P < 0.05) (5ig. 4A and 5B). The relative abundance of Portiera had a significant positive correlation with the relative abundance of Hamiltonella (r = 0.296, P < 0.05), but a significant negative correlation with the relative abundance of Rickettsia (r = -0.886, P < 0.001) (Fig. 5C and 5D). Results from the generalized linear models revealed a significant correlation between Rickettsia and Hamiltonella (Table 2). Categorical analysis showed that Wolbachia and Arsenophonus significantly affected the abundance of each other positively (Fig. S1E and S2E).

Table 2

Generalized linear models of symbionts, bacterial diversities and environmental factors in *Bemisia tabaci* MED.
Dependent Variable	Covariates	B	Wald Chi-Square	P-value
Rickettsia	Hamiltonella	0.156	14.496	0.003
Portiera	X	0.039	14.913	< 0.001
	Y	-0.365	7.255	0.007
	Bio1	-0.202	9.324	0.002
	Bio12	0.003	10.085	0.001
	X*bio1	0.001	5.536	0.019
	X*bio5	-0.002	17.685	< 0.001
	X*bio12	-0.000014	9.398716	0.002
	Y*bio5	0.011	4.996	0.025
Rickettsia	X	-0.44	17.247	< 0.001
	Y	-0.365	7.255	< 0.001
	Bio1	-0.202	9.324	0.002
	Bio12	0.003	10.085	0.001
	X*bio1	0.001	5.536	0.019
	X*bio5	-0.002	17.685	< 0.001
	X*bio12	-0.000014	9.398716	0.002
	Y*bio5	0.011	4.996	0.025
Hamiltonella	X	0.006	4.132	0.042
Shannon	Y	0.312	4.396	0.036
	X*bio1	-0.001	6.702	0.010
	X*bio5	0.001	7.969	0.005
	X*bio12	0.000012	5.911	0.015
Simpson	X	-0.016	4.982	0.026
	Y	0.234	5.895	0.015
	X*bio1	-0.001	6.007	0.014
	X*bio5	0.001	8.947	0.003
	X*bio12	0.000009	7.838	0.005
	Y*bio5	-0.007	4.020	0.045
Chao1	Y	29.251	4.619	0.032
	X*bio12	0.001	5.763	0.016
	Y*bio1	0.361	4.817	0.028
	Y*bio5	-1.081	4.627	0.031
Note: “X” means “longitude”, “Y” means “latitude”.

Influence of ecological factors on the microbial communities in global Bemisia tabaci MED populations

To understand whether and to what extent ecological factors shaped the microbial communities across the B. tabaci MED populations, Pearson analysis and structural equation model (SEM) were used to resolve the relationships between microbial community structure (including symbionts and α-diversity indexes) and 5 putative predictor variables including annual mean temperature, mean temperature of warmest quarter, annual precipitation, latitude, and longitude. The diversity and abundance indexes of symbionts as influenced by climate or geographical factors were described by a structural equations model (SEM). Results showed that the annual mean temperature significantly and positively influenced the relative abundance of Rickettsia (r = 0.326, P < 0.05) (Fig. 6A), while annual precipitation significantly and positively influenced the Arsenophonus abundance (r = 0.325, P < 0.05) and the Chao1 index (r = 0.405, P < 0.01) (Fig. 6B and 6C) in global whitefly populations. Generalized linear models also revealed a significantly influence of ecological factors on the abundance of symbionts Portiera, Rickettsia and Hamiltonella (Table 2).

The relationships between the microbial communities of B. tabaci MED and longitude were also explored. Results showed that longitude positively influenced the abundance of Portiera (r = 0.499, P < 0.001) but negatively influenced the abundance of Rickettsia (r = -0.411, P < 0.01) (Fig. 7). Also, longitude negatively influenced the Shannon (r = -0.402, P < 0.01) and Simpson indexes (r = -0.321, P < 0.05) (Fig. 8).

The SEM analysis was used to clarify the complex interactions among symbionts, bacterial communities and ecological factors. The SEM models were simplified based on the lowest value of Akaike Information Criterion (AIC). The simplified models were not rejected based on the P value of chi-square test > 0.05, comparative Index (CFI) > 0.90, standardized root mean square residual (SRMR) < 0.05. For the relationships among different symbionts and ecological factors (Fig. 9). Results showed that longitude positively influenced Portiera abundance (r = 0.40 ± 0.12, z = 2.04, P < 0.05), but negatively influenced Rickettsia abundance (r = -0.42 ± 0.12, z = 3.40, P < 0.001). Further, longitude positively influenced Hamiltonella abundance but negatively influenced Wolbachia and Arsenophonus abundances. Latitude positively influenced both the abundance of Cardinium and Hamiltonella, but negatively influenced Rickettsia abundance. Annual mean temperature negatively affected Portiera abundance and positively affected Hamiltonella abundance, but mean temperature did not significantly influence Rickettsia abundance. The mean temperature of warmest quarter negatively influenced Hamiltonella abundance and Wolbachia abundance, but positively influenced Portiera abundance. Annual precipitation positively influenced Arsenophonus abundance and positively influenced Wolbachia abundance, but negatively affected Hamiltonella abundance. Also, SEM analysis indicated a negative correlation between Portiera and Rickettsia, as well as a negative correlation between Hamiltonella and Wolbachia. Additionally, SEM analysis also showed a negative correlation between Rickettsia and Cardinium abundance (r = -0.27 ± 0.12, z = 2.36, P < 0.05) (Fig. 9).

The relationships between the diversities of microbial communities and ecological factors were also explored by SEM (Fig. 10). Results showed that longitude negatively influenced the Shannon index, while latitude positively influenced both the Shannon and Chao1 indexes. Annual precipitation also positively influenced the Chao1 index, which result was similar to the Pearson analysis (Fig. 6C), and also positively influenced the Shannon index. Annual temperature and mean temperature positively influenced both the Shannon and Simpson indexes, while the mean temperature of warmest quarter negatively influenced both the Shannon and Simpson indexes (Fig. 10).

We collected 49 B. tabaci MED populations from 23 locations in Asia, Europe and USA, using techniques and experiments including 2bRAD-M sequencing, to explore the relationships among the microbial communities including symbionts in whiteflies and ecological factors. The results revealed that the structures of microbial communities in different B. tabaci MED populations were significantly different, and their diversities were significantly negatively affected by the relative abundance of Cardinium and Hamiltonella. The abundance of symbionts influenced one other, and the effect was always negative, except for Portiera and Hamiltonella. Many ecological factors including temperature, precipitation, longitude and latitude had effects on symbiont abundance and bacterial diversities in B. tabaci. Our results clarified a significant influence of symbiont infection on the microbial diversities in B. tabaci MED, as well as exploring the impacts of ecological factors on the bacterial communities including symbionts infection and microbial diversities.

In this experiment, we identified six symbionts including one primary symbiont Portiera and five secondary symbionts from all B. tabaci MED samples. These results are similar to that from other studies which also revealed that whiteflies are co-infected with a variety of symbionts globally [58,59,60,61,62]. The results confirmed that the primary symbiont Portiera is the most abundant bacteria in almost all B. tabaci MED populations (Fig. 2), which plays many important roles in whiteflies such as provision of essential amino acids [63,64,65,66]. Also, the results showed that Hamiltonella infected all B. tabaci MED populations, although it showed lower abundance (lower than 18.37% in all populations). The co-infection of Hamiltonella and Portiera was prevalent in whiteflies and is vital for B. tabaci MED. A previous study revealed that Hamiltonella completes the missing steps in some of the pathways of Portiera to provide a full complement of nutrients to their whitefly hosts [32]. Arsenophonous also infected many populations such as the AH, BH, CY and IL populations, and provided host whiteflies with B vitamin, similar to Hamiltonella [67].

The existence and abundance of different symbionts in insects promote interactions among them, especially for secondary symbionts [41,68,69,70]. For example, Wolbachia infection in the fruit fly, Drosophila neotestacea, significantly promoted the infection of Spiroplasma [69]. Similar results reported from wild Laodelphax striatellus populations showed that Wolbachia infection enriched the abundance of other bacteria groups including Thermus, Spiroplasma and Ralstonia [41]. Our 2bRAD-M sequencing results showed that the presence of Hamiltonella significantly and negatively influenced the abundance of Rickettsia and Wolbachia, while the presence of Portiera positively influenced Hamiltonella, but negatively influenced Rickettsia abundance (Fig. 4). The positive correlation between Portiera and Hamiltonella confirms the cooperation to provide a full complement of nutrients to their whitefly hosts [32], and which helps to promote their spread in whitefly populations. On the other hand, antagonistic interactions among bacteria including symbionts in other insects have been reported. For example, the relative abundance of 154 bacteria genera in Laodelphax striatellus (SBPH) significantly decreased with Wolbachia infection [41]. In the D. melanogaster gut, the prevalence of various bacteria is generally negatively correlated [71] and many isolated bacteria inhibit the growth of the primary symbiont Paenibacillus larvae in the honeybee [68]. Another study reported of an antagonistic interaction between Hamiltonella and Cardinium in B. tabaci [72]. The co-sharing of the bacteriocytes in B. tabaci by symbionts may promote such antagonistic interactions, as a result of competition for limited space and resources in host whiteflies [31,73,74].

The infection and titer of symbionts in arthropods including in whiteflies are affected by many ecological factors including temperature, precipitation, longitude, latitude and so on [25,43,44,45,70]. For example, Cardinium infection in the spider mite, Tetranychus truncatus is positively influenced by latitude but decreases with increased annual precipitation [70]. We are the first to report that longitude and latitude significantly affect the abundance of many symbionts including Portiera, Rickettsia and Cardinium in whiteflies (Figs. 6 and 8). Our result showed a positive influence of latitude on Cardinium abundance, as well as a negative influence of precipitation on Hamiltonella. However, precipitation positively influenced the abundance of Wolbachia and Arsenophonus and temperature postively affected Rickettsia abundance (Fig. 4A), which is congruent to a previous study which revealed the Rickettsia infection significantly increased the thermotolerance of host whiteflies [37]. The frequency and abundance of symbionts are significantly influenced by temperature, especially for Cardinium [43,44,45]. We previously reported that Cardinium infection increased the thermotolerance of host whiteflies [9]. However, in this study the correlation between the abundance of Cardinium and temperature was not significant. Therefore, the relationships between Cardinium and temperature in whiteflies should be further explored. Results showed that annul mean temperature had negative effects on Portiera abundance. A previous study revealed that Portiera titer was significantly reduced under high temperatures [75].

There is increasing awareness of the vital role of the microbiome on host insects such as the provisioning of essential nutrients, synthesis of specific toxins or modification of the insect immune system [13,76]. However, the structure and diversities of microbial communities in insects are influenced by many factors, including host sex, growth stages, the abundance of symbionts, antibiotics as well as many geographic and climatic factors [13,41,66,77,78]. In this experiment, we revealed that the diversity of whiteflies’ bacterial communities (Shannon, Simpson and Chao1 indexes) is significantly influenced by many ecological factors including temperature, precipitation, longitude and latitude (Fig. 4C, 6 and 7). Similar results were also reported on the SBPH, whic showed that the microbial structure in SBPH was also significantly influenced by annual mean precipitation and longitude [41], while the microbial communities in stoneflies were significantly influenced by geographical distances [46]. How ecological factors influence the structure of microbial communities in B. tabaci MED should be explored further, although studies have shown that insects acquire microbiome from the environment [79,80,81].

Our results also showed that the infection and abundance of many symbionts in whiteflies were significantly influenced by ecological factors (Fig. 6A, 6B, 7 and 9), which results are similar to other related studies [43,70,82]. This indicate that the change in symbiont abundance caused by ecological factors may indirectly alter the diversity of bacterial community in B. tabaci MED. We discovered that symbiont abundance had significant effects on the diversities of microbial communities in whiteflies, which results are congruent with that in other related studies [40,41,42]. For example, Wolbachia infection significantly changed the structure of microbial communities in SBPH [41] and D. melanogaster [42]. The complex interactions among different symbionts and microbial communities in insect host should be explored further.

In summary, 2bRAD-M sequencing analysis of 49 global B. tabaci populations provided a new and comprehensive perspective of the relationship among the structure and diversity of bacterial community, ecological factors as well as the symbionts in B. tabaci MED populations. The results showed that the symbionts within host whiteflies affected one another, and the abundance of symbionts and microbiota diversities were significantly influenced by many ecological factors. However, further studies are needed to clarify how ecological factors influence the diversity of bacterial communities in different B. tabaci MED populations under global ecological timescales.

Author Contributions: Conceptualization, Chu D; sample preparation: Li HR; methodology and data analysis: Yang K, Zhang YX, Zhou JC and Zhang YJ; writing and editing: Yang K, Zhang YX and Chu D; funding acquisition: Chu D and Yang K.

Acknowledgments: The authors wish to acknowledge Dr Ary Hoffman, for the help in analyzing the data of the results of this study.

Funding

This research was supported by the National Key Research and Development Program of China (2022YFD1401200), the Science and Technology Benefiting the People Demonstration Project of Qingdao (24-1-8-xdny-10-nsh), the Taishan Scholar Foundation of Shandong Province, and the Qingdao Agricultural University High-level Talent Fund (663-1121025).

Availability of data and materials

The raw data of the 2b-RAD sequencing were deposited in the Sequence Read Archive Database of the NCBI (BioProject number: SRR10541684).

Ethics approval and consent to participate

All authors consent to participate in this study.

Consent for publication

All authors give their consent for publication.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M

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Abstract

Background

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Introduction

Materials and methods

DNA extraction and 2bRAD-M sequencing

Data analysis of 2bRAD-M sequencing results

Results

Discussion

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References

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