Stochastic processes govern bacterial communities

FEMS Microbiology Ecology, 94, 2018, fiy082 doi: 10.1093/femsec/fiy082 Advance Access Publication Date: 0 2018 Research Article

RESEARCH ARTICLE

Stochastic processes govern bacterial communities from the blood of pikas and from their arthropod vectors Huan Li1 , Tongtong Li2 and Jiapeng Qu3,4, * 1

Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou 730000, China, 2 Department of Applied Biology, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou 310014, China, 3 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai 810008, China and 4 Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Region, Qinghai 810008, China ∗

Corresponding author: 23#Xinning Lu,Xining,Qinghai,China; Tel: +86-971-6143610 Fax: +86-971-6143282; E-mail: [email protected]

One sentence summary: Stochastic processes govern the assembly of bacterial communities from the blood of pikas and from their arthropod vectors. Editor: Julian Marchesi

ABSTRACT Vector-borne microbes influence pathogen transmission and blood microbiomes, thereby affecting the emergence of infectious diseases. Thus, understanding the relationship between host and vector microbiomes is of importance. In this study, we investigated the bacterial community composition, diversity and assembly of the flea (Rhadinopsylla dahurica vicina), torsalo (Hypoderma curzonial), and the blood and gut of their shared pika host, Ochotona curzoniae. Bartonella, Sphingomonas and Bradyrhizobium were enriched in blood, while Wolbachia and Fusobacterium were more abundant in fleas and torsaloes. Most of potential pathogenic microbes (belonging to Fusobacterium, Rickettsia, Kingella, Porphyromonas, Bartonella and Mycoplasma) were present in the blood of pikas and their vectors. Blood communities were more similar to those from fleas than other sample types and were independent of host factors or geographical sites. Notably, blood microbes originate mainly from fleas rather than gut or torsaloes. Interestingly, the community assembly of blood, fleas or torsaloes was primarily governed by stochastic processes, while the gut microbiome was determined by deterministic processes. Ecological drift plays a dominant role in the assembly of blood and flea microbiomes. These results reflect the difficulty for predicting and regulating the microbial ecology of fleas for the prevention of potential microbiome-associated diseases. Keywords: blood; flea; microbiome; diversity; stochastic processes; ecological drift

INTRODUCTION Humans and animals have close contacts with microorganisms. These microbes reside in hosts and constitute diverse microbial communities, including beneficial symbionts, opportunists and pathogens (Kikuchi, Hosokawa and Fukatsu 2007;

Tremaroli and Backhed 2012; Wu et al. 2012). The composition and diversity of these microbiomes may significantly influence host ecology and evolution. For example, bacterial symbionts from hosts may increase the utilization efficiency of food resources (Tsuchida, Koga and Fukatsu 2004), improve the tolerance to extreme environment (Montllor, Maxmen and Purcell

; Accepted: 1 May 2018 C FEMS 2018. All rights reserved. For permissions, please e-mail: [email protected]

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FEMS Microbiology Ecology, 2018, Vol. 94, No. 00

2002) and exhibit strictly convergent patterns of genome reduction through vertical transmission from mothers to offspring (Moran, McCutcheon and Nakabachi 2008). Application of highthroughput sequencing techniques revealed the complex and diverse bacterial communities of humans (Zhang et al. 2015), laboratory animals (Carmody et al. 2015) and wild animals (Li et al. 2017b; Li et al. 2017c). Of particular interests are arthropod vectors, including fleas (Cohen et al. 2015), ticks (Rynkiewicz et al. 2015), mosquitoes (Osei-Poku et al. 2012), mites (Reeves, Dowling and Dasch 2006), flies (Hornok et al. 2008) and leeches (Maltz et al. 2014). These vector-borne microbes can potentially influence vectorial capacity (Jackson et al. 2003) and pathogen transmission (Van Den Abbeele et al. 2010). Unfortunately, most studies only focused on several specific vector-borne bacterial pathogens, such as Yersinia pestis (etiological agent of bubonic plague), Borrelia burgdorferi (causative agent of Lyme diseases), Rickettsia felis (etiological agent of Rickettsial diseases) and Plasmodium spp.(causative agent of avian malaria) (Parola and Didier 2001; Parola, Paddock and Raoult 2005; Jones, Knight and Martin 2010; Andreotti et al. 2011), whereas the effects of the whole microbiomes from vectors on host fitness were not fully studied. Evidence has shown that several vector-associated (e.g. fleas, fruit flies and aphids) bacterial symbionts could exclude or decrease the densities of harmful parasites (Sakurai et al. 2005; Jaenike et al. 2010; Jones et al. 2012). Therefore, describing the microbiomes of arthropod vectors and hosts may improve our understanding for the relationship between hosts and vectors, pathogen transmission and potential functions of their bacterial communities. Previous studies demonstrated that blood in healthy mammals is sterile and cannot accommodate proliferation of microorganisms (Drennan 1942). However, recent reports demonstrated that the blood of both humans and small rodents harbors diverse microbial communities (Sato et al. 2014; Cohen et al. 2015; Paisse et al. 2016), thereby challenging the previous assumption. Although microbiomes are possibly dormant in the host blood, the diversity of blood bacterial communities is associated with a variety of diseases, such as severe bacteremia (Huang et al. 2006) and Parkinson’s disease (Nielsen et al. 2012). Given that the blood of hosts is exposed to arthropod vectors, blood microbial diversity may be closely associated with microbiota diversity of their arthropod vectors, such as fleas and torsaloes. These two vectors derive nutrition from the host blood throughout their life cycle and may thus transmit bacteria into the blood. In addition, host gut contains numerous capillary vessels, increasing the likelihood of gut microbes entering the bloodstream. Thus, gut and ectoparasite microbial communities of hosts may be potential bacterial reservoirs for blood microbiomes. However, up to date, relatively little is known regarding the diversity and origin of blood microbes in wild mammals. Host and environmental factors have been reported to influence gut microbial communities in laboratory animals (Org et al. 2015). However, the microbiomes of wild animals and their arthropod vectors may also be affected by host characteristics (i.e. host sex, body weight and age) and environmental conditions. Identifying the potential host and environmental factors that influence the bacterial communities may prevent and control diseases associated with microbiomes (Lemon et al. 2008). In addition, host immune response may also regulate microbial interactions within the blood or vectors and abundance of vector-borne microbes, thus influencing the pathogen transmission and virulence to human hosts (Telfer et al. 2010; Cirimotich et al. 2011; Rynkiewicz 2013). Thus, studies should identify host

and environmental factors that may affect blood and vectorborne microbes. In recent years, several studies have elucidated the composition and diversity of the blood of hosts and their vectors (Pornwiroon et al. 2007; Jones, Knight and Martin 2010; Gutierrez et al. 2014; Cohen et al. 2015; Rynkiewicz et al. 2015). However, to our knowledge, few studies focused on how ecological processes govern microbiomes in host-vector systems. Recent studies showed that microbial community assembly is governed by stochastic processes, deterministic processes or both (Stegen et al. 2012; Stegen et al. 2013; Zhou et al. 2014). When a community is assembled primarily based on stochastic processes, the community can be driven by stochastic events, such as stochastic dispersal, colonization and local extinction. Community variation may also be unpredictable (Purves and Turnbull 2010; Rosindell et al. 2012). If a community is governed mainly by deterministic processes, selection or filtering may lead to similar communities under similar environmental conditions (Zhou et al. 2014). Although the assembly mechanisms of microbial communities in water (Zhou et al. 2014), soil (Dini-Andreote et al. 2015) and fish gut ecosystems (Burns et al. 2015) have been well studied, the relative contribution of deterministic processes versus stochastic processes is dynamic and changeable in different ecosystems. Understanding the assembly processes of microbiomes in host-vector systems may aid in prediction and regulation of pathogenic transmissions associated with diseases. In addition, a finer division of ecological mechanisms underlying the community assembly includes four major processes: selection, dispersal, drift and speciation (Vellend 2010). These ecological processes may contribute to the assembly of microbial communities in fish gut ecosystems (Yan et al. 2016) but remain largely unknown in mammal-vector systems. An ecological view based on theoretical framework may interpret microbiome configurations and infer how environmental and host factors affect these patterns, and prevent transmission of vectorborne pathogens. Plateau pika (Ochotona curzoniae) is a member of the Ochotonidea family (Order Lagomorpha) and is widely distributed in the Qinghai-Tibet Plateau. Under moderate population density, plateau pikas increase plant diversity, aid in soil formation, aeration and mixing, and improve water infiltration into the soil (Li et al. 2013). Plateau pikas occasionally possess high population density and are considered as pests that degenerate grasslands. In the wild, plateau pikas carry ectoparasites that may transmit pathogens. Fleas (Rhadinopsylla dahurica vicina) and torsaloes (Hypoderma curzonial) are two common ectoparasites of plateau pikas. These ectoparasites feed on pika blood, which may lead to the risk of microbiome-associated diseases. Our previous studies focused on the relationship between pika gut microbiome and host (host phylogeny, gut region and population density) or environment (diet and environmental microbes) (Li et al. 2016a,b,c, 2017a,b). However, the relationship between ectoparasite microbiomes and host microbiomes in small lagomorphs remains unknown. In the present study, we sampled the blood, gut of pikas and their flea and torsalo vectors. MiSeq sequencing of bacterial DNA was used to test whether cooccurring ectoparasites and hosts would have similar microbiomes. In particular, we addressed the following questions: (i) Which bacterial taxa (including potential pathogens) constitute the bacterial communities of fleas, torasloes and blood of their pika hosts?; (ii) What are the differences between alpha and beta diversity across sample types, and do host factors and environmental conditions influence microbiomes in host-vector systems?; (iii) What is the possible origin of blood microbiomes?

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Which microbes can be colonized into the host blood? (IV) Is the assembly of bacterial communities governed by stochastic or deterministic processes in host-vector systems?

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and then subjected to 250 bp paired-end sequencing in an Illumina MiSeq system (Illumina, San Diego, CA, USA).

Bioinformatics analysis

MATERIALS AND METHODS Two geographical sites were sampled for the gut and blood of plateau pikas (O. curzoniae) and their ectoparasites, including flea (Rhadinopsylla dahurica vicina) and torsalo (Hypoderma curzonial) in the summer (May and June) of 2016. The fleas belong to the Pulicidae family of the order Siphonaptera, whereas the torsaloes belong to the Muscidae family of the order Diptera. These two sampling sites were located in the district Reshui (37◦ 30 31 N, 100◦ 28 19 E) and Haibei (37◦ 45’24 N, 101◦ 21’27E ) on the Qinghai-Tibet Plateau. A total of 42 pikas were captured using rope and then euthanized. The fleas and torsaloes from the skin and fur of pikas were collected into 5m lsterile microtubes with half of tube containing 70% ethanol in a portable freezer at −0 ◦ C. Each flea or torsalo was identified at the species level based on their morphological characteristics and stored individually. Notably, the torsaloes were in larval stage and attached to the skin of pikas. After sample collection, 100– 200 μl blood samples were drawn from the retro-orbital sinus (Rynkiewicz et al. 2015) of each pika and kept in blood collection tubes containing eathylene diamine tetraacetic acid, and placed inside the portable freezer at −20 ◦ C. After euthanizing and dissecting the pikas, cecal contents were collected and placed into 50 ml sterile tubes. Given that only a portion of host individuals was infected with fleas (infection rate = 52.4%) or torsaloes (23.8%), we finally collected a total of 125 samples, including 42 blood samples, 42 gut samples, 26 flea samples and 15 torsalo samples. We identified and measured host characteristics, including sex, body weight, body length and age (Juvenile, adult). Finally, all samples were transferred to our laboratory within 24 h and stored at −40◦ C until DNA extraction. All animal experiments and relevant guidelines were approved by the Animal Care and Ethics Committee of Northwest Institute of Plateau Biology, Chinese Academy of Sciences (NWIPB-2016-120). Experimental procedures followed the relevant provisions strictly.

All original sequences were processed using QIIME PipelineVersion 1.7.0 (http://qiime.org/scripts/index.html). Briefly, sequences were assigned to each sample only if they perfectly matched their unique barcodes. Thereafter, sequence merging, trimming, filtering and analysis were performed according to the methods of Li et al. (2016c). After filtering out low-quality sequences, chloroplasts, chimeras and sequences not classifying to bacteria, the remaining sequences were clustered into operational taxonomic units (OTUs) at 97% sequence similarity with an open-reference OTU picking method using the Uclust algorithm (Edgar 2010). Singletons were also removed. PCR bands were not detected in the negative controls, indicating minimum contamination in our study. The most abundant sequence for each OTU was considered as the OTU’s representative sequence, and these sequences were aligned against the Greengenes 13 8 reference database (DeSantis et al. 2006) using PyNAST (Caporaso et al. 2009). Thereafter, taxonomic classification was performed using the Ribosomal Database Project classifier with a standard threshold of 80% (Wang et al. 2007) and further identified, if necessary, using BLASTn analysis again the GenBank database (http://www.ncbi.nlm.nih.gov). The samples with less than 3686 sequences were removed before standardization. The final sample size comprised 19 blood samples, 42 gut samples, 19 flea samples and 9 torsalo samples. Thereafter, each sample was rarefied to 3686 sequences to compare community diversity between groups. To assess alpha diversity of bacterial communities, Good’s coverage, observed OTUs, Shannon diversity, phylogenetic diversity, Chao1 and evenness were calculated across groups. The OTUlevel rarefaction curve of observed OTUs across groups was calculated. To evaluate beta diversity indices, unweighted (for community membership) and weighted (for community structure) UniFrac distances (Hamady, Lozupone and Knight 2010) were calculated using QIIME pipeline. Differences in overall bacterial community membership and structure were visualized using Principal Coordinate Analysis (PCoA) plots of these dissimilarity metrics.

Sample preparation and high-throughput sequencing

Statistical analysis

For each host individual, one flea or torsalo was randomly selected if multiple parasites existed in one pika individual. Fleas and torsaloes were washed in 70% ethanol at least five times to remove the microbes associated with their surfaces. Thereafter, Ezup DNA Extraction Kit for soil (Sangon Biotech, China) was used to extract DNA from these two parasites and gut samples following the manufacturer’s instructions. To extract DNA from the blood samples, Bacteremia DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA) was used according to the manufacturer’s instructions. We used sterile water as negative control in our extraction process. Universal primers 515F/909R with 12 nt unique barcode were used to amplify the V4–V5 hyper-variable region of the 16S rRNA gene (Tamaki et al. 2011) of our microbiome samples. Each polymerase chain reaction (PCR) contained a negative control where sterile water was used. PCR amplification was performed twice for each DNA sample and negative control. The detailed PCR conditions and procedures were described previously (Li et al. 2016c). Finally, all PCR products were mixed with equal molar,

Analysis of similarity (ANOSIM) (Warton, Wright and Wang 2012) based on the unweighted and weighted UniFrac distance matrices was used to elucidate whether the community membership and structure were significantly different across sample types (blood, gut, flea and torsalo) using the procedure ‘anosim’ in the R ‘vegan’ package. ANOSIM was also used to explore the effects of host characteristics and sampling sites in shaping the bacterial communities of blood, gut, flea or torsalo. The host characteristics included the following factors: sex, body weight, body length and age (Juvenile, adult). Taxonomic profiles were evaluated at the phylum and genus levels. Differences in relative abundances of the genera were performed through group significance.py script with Student’s T-test. Only those genera with mean relative abundance >0.5% in at least one sample group were considered. One-way-analysis of variance (one-way ANOVA) with post-hoc tests was used to test the differences in the alpha diversity indices. P-value has been corrected using the false discovery rate control. In addition, the unweighted and weighted UniFrac dissimilarities of bacterial

Sample collection and ethical standards

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communities between groups were also calculated using the command line make distance boxplots.py in QIIME platform, and one-way ANOVA with post-hoc tests was used to test pairwise differences in dissimilarities. To understand the potential transmission of host blood and ectoparasite bacterial communities, a Venn diagram was created using the program VennDiagram in R to visualize the shared and unique OTUs among the blood, gut, fleas and torsaloes. We randomly extracted nine samples from each group to create a Venn diagram to exclude the effects of uneven sampling size. To investigate the relationship between blood microbes and others, we calculated the mean relative abundances of shared OTUs between the blood and gut, fleas or torsaloes. Mean relative abundances of shared OTUs were calculated by dividing the sum of relative abundances across all samples in a group by the total number of samples in that group.

Predicted metagenomes Functional metagenomes were predicted based on the 16S rRNA sequences using PICRUSt (Langille et al. 2013). To evaluate the prediction accuracy of PICRUSt analysis in our metagenomic samples, we calculated the Nearest Sequenced Taxon Index (NSTI), which indicates the average divergence between each 16S rRNA sequence within an OTU and their closest reference sequenced genome. The average NSTI values for our samples (0.11±0.06) suggested the comparable predictive accuracy of our communities with those achieved for mammalian and fish gut microbiota (Langille et al. 2013; Sullam et al. 2015). Significant differences in gene functions (t-tests, Bonferroni-corrected) between the blood and gut, fleas or torsaloes were evaluated at level 2 using the group significance.py script in QIIME pipeline.

Estimation of ecological processes Community assembly is governed by deterministic processes, stochastic processes or both (Stegen et al. 2012; Stegen et al. 2013; Zhou et al. 2014). Nearest Taxon Index (NTI) and Mean-nearesttaxon-distance (MNTD) were used to qualitatively evaluate the deterministic or stochastic processes of community assembly (Meyerhof et al. 2016). These values were calculated using the picante package. The ses.MNTD command with null model = ‘taxa. labels’, abundance.weighted = TRUE, 1000 randomizations was used. NTI is equal to the inverse of ses. MNTD is calculated as the difference between the observed MNTD and mean expected MNTD divided by the standard deviation of random expected values. If the NTI values are higher than −2 but less than 2, and then we can conclude that community assembly is mainly governed by stochastic processes. If the NTI values are higher than 2 or less than −2, then we can conclude that deterministic processes play more important roles in structuring microbial communities (Meyerhof et al. 2016). We followed the methods of Stegen et al. (2013) to calculate the potential ecological processes (variable selection, homogeneous selection, dispersal limitation, homogenizing dispersal or drift) that govern bacterial community assembly of each group. Briefly, we calculated the phylogenetic diversity of bacterial communities between a pair of samples. R package was used to quantify the weighted beta nearest taxon index (β-NTI). The combination of β-NTI and Bray–Curtis-based Raup-Crick (RCbray ) was further used to infer the relative importance of major ecological processes governing the bacterial communities. Values of β-NTI > 2 or < −2 represent community turnover determined

by variable or homogeneous selection, respectively. If −2 < βNTI< 2 and RCbray >0.95 or < −0.95, then community turnover is determined by dispersal limitation or homogenizing dispersal, respectively. If 2 < β-NTI< 2 and −0.95< RCbray < 0.95, community turnover is a drift process (referred to as ‘undominated’ process) (Stegen et al. 2015).

Nucleotide sequence accession numbers The original 16S rRNA data are available at the European Nucleotide Archive by accession NO. PRJEB21945 (http://www.eb i.ac.uk/ena/data/view/PRJEB21945).

RESULTS Blood, gut and ectoparasites microbiomes A total of 3660 077 original 16S rRNA gene sequences were obtained. After quality control and sample standardization, we obtained 328054 sequences from 89 samples, and 11579 unique OTUs were clustered at 97% sequence similarity. The blood of pikas had diverse bacterial community compositions (Fig. 1). At the phylum level, blood microbiome was mainly dominated by Proteobacteria (mean relative abundance = 70.32%), Bacteroidetes (6.82%), Tenericutes (6.35%), Firmicutes (5.29%), Actinobacteria (3.76%) andAcidobacteria (1.54%). Other rare phyla (mean relative abundance < 1%) included Cyanobacteria, Chloroflexi, Planctomycetes, Verrucomicrobia, Gemmatimonadetes, Spirochaetes, Nitrospirae and Chlorobi. In contrast, the dominate phyla in fleas were Proteobacteria (76.37%), Bacteroidetes (7.45%), Firmicutes (6.42%) and Actinobacteria(1.57%), whereas torsalo-associated microbiomes mainly consisted of Proteobacteria(84.47%), Fusobacteria(6.68%), Bacteroidetes (3.95%) and Firmicutes (3.13%). However, pika gut microbiomes exhibited different community compositions from those of blood microbiome and were dominated by Bacteroidetes (44.35%), Firmicutes (37.30%), Proteobacteria (9.72%), Spirochaetes (4.04%) and Tenericutes (1.05%). At the genus level, the identified genera, including Bartonella (21.05%), Sphingomonas (2.39%) and Bradyrhizobium (1.40%), were enriched in the blood, whereas the seven dominant genera in fleas were Wolbachia (19.93%), Bartonella (14.8%), Rickettsia (8.02%), Exiguobacterium (2.37%), Acinetobacter (1.58%), Sphingomonas (1.33%) and Prevotella (1.11%). Those predominant genera in torsaloes comprised Fusobacterium (6.66%), Kingella (5.65%), Porphyromonas (3.3%) and Veillonella (1.37%). Incontrast, Prevotella (12.74%) was the most abundant genus in the pika guts. A total of 12 bacterial genera, such as Lactobacillus, Bartonella, Loktanella, Prevotella, Oscillospira and Acinetobacter, demonstrated significant differences between blood and gut microbiomes (Table S1, Supporting Information). The relative abundances of Wolbachia, Exiguobacterium and one unknown genus from Oxalobacteraceae significantly differed in the blood and in the fleas (all P < 0.05). Only one genus from Pasteurellaceae demonstrated significantly different abundance between blood and torsaloes (P < 0.05; Table S1, Supporting Information).

Abundance and distribution of dominant and pathogenic OTUs The top 20 OTUs across all samples comprised over 49% of the total sequences. Among these OTUs, OTUs 3 and 8 were mainly found in the blood (Table 1). Their best taxonomic matches were Bartonella heixiaziensis and Mollicutes (class). In contrast, OTUs

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Figure 1. Bacterial community composition from the blood, gut of pikas and from their flea and torsalo vectors at phylum level. Only phyla with mean relative abundance >0.1% across all samples are shown.

Table 1. Twenty most abundant OTU clusters (97% similarity) listed from most to least abundant, with cumulative percentages of sequences from each OTU, proportion of sequences from total rarefied sequences and from each sample type (blood, gut, flea and torsalo) and their taxonomic classification.

OTU

Total rarefied Proportion sequences from torsaloes

Proportion from fleas

Proportion from blood

Proportion from gut

1

46 587

0.0378

0.3454

0.3829

0.2340

2

27 141

0.8326

0.0122

0.1551

0.0001

3

23 872

0.0001

0.4279

0.5720

0.0001

4

13 948

0.0000

0.9999

0.0001

0.0000

5

5582

0.0000

0.9984

0.0016

0.0000

6

5437

0.0051

0.0294

0.0292

0.9362

7

4315

0.0028

0.0378

0.0243

0.9351

8 9

3991 3792

0.0000 0.0361

0.0000 0.2120

1.0000 0.3755

0.0000 0.3763

10 11 12

3421 2812 2720

0.0018 0.0000 0.0037

0.0295 0.9996 0.0224

0.0164 0.0004 0.0309

0.9524 0.0000 0.9430

13

2483

0.0064

0.0302

0.0238

0.9396

14

2248

0.0022

0.0071

0.0151

0.9755

15

2243

0.9844

0.0156

0.0000

0.0000

16

2232

0.0332

0.1882

0.4400

0.3387

17

2145

0.0028

0.0364

0.0326

0.9282

18

2070

0.0000

0.0314

0.0101

0.9585

19

2027

0.0084

0.0301

0.0331

0.9285

20

1998

0.0035

0.0210

0.0185

0.9570

Taxonomic classification Proteobacteria; Betaproteobacteria; Burkholderiales; Oxalobacteraceae; Undibacterium; Undibacterium oligocarboniphilum Proteobacteria; Gammaproteobacteria; Pasteurellales; Pasteurellaceae; Mannheimia; Mannheimia caviae Proteobacteria; Alphaproteobacteria; Rhizobiales; Bartonellaceae; Bartonella; Bartonella heixiaziensis Proteobacteria; Alphaproteobacteria; Rickettsiales; Rickettsiaceae; Wolbachia Proteobacteria; Alphaproteobacteria; Rickettsiales; Rickettsiaceae; Rickettsia; Rickettsia australis Firmicutes; Clostridia; Clostridiales; Ruminococcaceae; Oscillospira Bacteroidetes; Bacteroidia; Bacteroidales; Prevotellaceae; Prevotella Tenericutes; Mollicutes Proteobacteria; Alphaproteobacteria; Sphingomonadales; Sphingomonadaceae; Sphingomonas; Sphingomonas echinoides Bacteroidetes; Bacteroidia; Bacteroidales Unclassified Bacteria Bacteroidetes; Bacteroidia; Bacteroidales; Prevotellaceae; Prevotella Firmicutes; Clostridia; Clostridiales; Clostridiaceae; Clostridium Spirochaetes; Spirochaetes; Spirochaetales; Spirochaetaceae; Treponema Fusobacteria; Fusobacteriia; Fusobacteriales; Fusobacteriaceae; Fusobacterium; Fusobacterium gastrosuis Proteobacteria; Alphaproteobacteria; Rhizobiales; Bradyrhizobiaceae; Bradyrhizobium Bacteroidetes; Bacteroidia; Bacteroidales; Prevotellaceae; Prevotella Bacteroidetes; Bacteroidia; Bacteroidales; Rikenellaceae; Alistipes Firmicutes; Clostridia; Clostridiales; Ruminococcaceae; Oscillospira Bacteroidetes; Bacteroidia; Bacteroidales; Prevotellaceae; Prevotella

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4, 5 and 11 were mainly found in the fleas of pikas and were matched to Wolbachia, Rickettsia australis and Unclassified Bacteria, respectively. Different from fleas, OTUs 2 (belonging to Mannheimia caviae) and 15 (Fusobacterium gastrosuis) were widely distributed in the torsalo samples. In addition, a total of 10 OTUs mainly originated from the gut samples. The best taxonomic identification for these OTUs included Prevotella, Oscillospira, Treponema and Alistipes (Table 1). Several potential pathogenic bacteria, such as Fusobacterium, Rickettsia, Francisella, Kingella, Porphyromonas, Plasmodium, Yersinia, Bartonella, Borrelia, Mycoplasma, Ehrlichia, Streptobacillus and Orientia, may lead to large-scale emergence and prevalence of infectious zoonotic diseases (Clay et al. 2008; Jones, Knight and Martin 2010; Andreotti et al. 2011; Wang et al. 2011; Galan et al. 2016). Exploring the abundance and distribution of these microbes may help us prevent and control infectious diseases caused by transmission of vector-borne pathogens. A total of six potential pathogenic genera, including Fusobacterium (2 OTUs), Rickettsia (10 OTUs), Kingella (1 OTU), Porphyromonas (3 OTUs), Bartonella (76 OTUs) and Mycoplasma (2 OTUs) were detected in our samples (Table 2), and their relative abundances across all samples were 0.68%, 1.72%, 0.57%, 0.34%, 7.65% and 0.12%, respectively. Three pathogenic genera Fusobacterium, Kingella and Porphyromonas were mainly distributed in the torsalo samples, whereas Rickettsia was mainly distributed in flea samples. Notably, 99.74% sequences from Mycoplasma OTUs were present in blood samples, but completely absent in the flea or torsalo samples. The proportion of sequences from all Bartonella OTUs in the blood and fleas were 58.70% and 41.27%, respectively. However, both Bartonella OTUs were present in the torsalo or gut samples at very low abundances (both 0.008%).

Differences in alpha diversity between blood and flea, torsalo or gut microbiomes The OTU-level rarefaction curves of the observed OTUs across four sample types nearly reached a plateau (Fig. S1, Supporting Information), indicating that our sequencing data captured most bacterial species in these environmental samples although additional sequencing depth may obtain several rare OTUs. In addition, the Good’s coverage across all samples reached 91.34±4.24%, confirming that sequencing depth per sample sufficiently covers a majority of bacterial community diversity in this study. The alpha diversity indices, including observed OTUs, Shannon diversity, phylogenetic diversity, Chao1 and evenness, were the highest in the pika gut, followed by blood, flea and torsalo (Table 3; Fig. S1, Supporting Information). The alpha diversity values of the blood and fleas were higher than torsaloes, whereas blood community diversity showed no significant difference from that in the flea vectors.

Differences of beta diversity between sample types Overall, PCoA plots based on unweighted and weighted UniFrac distances showed distinguishable community memberships and structures across different sample types (Fig. 2). ANOSIM indicated that sample type was a significant predictor of bacterial community membership (r = 0.756, P < 0.001) and structure (r = 0.727, P < 0.001). Blood microbiome was significantly different from gut microbiome (unweighted UniFrac r = 0.92, P < 0.001; weighted UniFrac r = 0.727, P < 0.001). Despite a partial overlap, the bacterial communities of the blood from pikas exhibited different community memberships and structures with their ectoparasite fleas and torsaloes (Table 4). In addition, we also

compared the unweighted and weighted dissimilarities of these four sample types. We found that blood microbiome was more similar to flea microbiome than the other sample types based on the unweighted and weighted matrices (Fig. 3). ANOSIM was further used to explore whether host characteristics and geographical locations may influence the blood, gut, flea and torsalo microbiomes (Table S2, Supporting Information). Host sex, body weight and length showed no significant effects on the gut microbiome, while host age exhibited significant effects on the assembly of gut communities (unweighted UniFrac r = 0.131, P = 0.008; weighted UniFrac r = 0.25, P < 0.001). For blood, fleas or torsalo microbiomes, host sex, age, body weight and length showed no significant effects (all P > 0.05). In addition, we noted that sampling sites significantly affected the gut microbiome (unweighted UniFrac r = 0.063, P < 0.05; weighted UniFrac r = 0.055, P < 0.05), whereas no significant effects were observed in the microbial communities of blood, fleas or torsaloes (all P > 0.05).

Origin and selection of blood microbial communities Mammalian blood was generally considered sterile at birth (Potgieter et al. 2015) and may acquire microbes by transmission. Host gut and ectoparasite microbiomes are potential reservoirs for the blood of pikas. To understand the potential origin of blood microbes, a Venn diagram was created after we randomly extracted nine samples from each group (Fig. 4A). The unique total number of OTUs from all groups was 7652, and the torsalo, flea, blood and gut microbiomes consisted of 767, 2243, 3706 and 3800 OTUs, respectively. Overall, the percentages of specific OTUs across all OTUs from torsalo, flea, blood and gut microbial communities reached 2.95%, 10.04%, 27.31% and 33.13% (Fig. 4A). Approximately, 30.09% OTUs (1115 of 3706 total OTUs) of the blood samples originated from fleas, whereas only 23.77% (881 of 3706 total OTUs) and 11.17% OTUs (414 of 3706 total OTUs) from blood microbial communities were present in the gut and torsaloes, respectively. Notably, the specific OTUs in the pika blood samples reached more than half (2090 of 3706 total OTUs) of total OTUs. For fleas, 49.71% OTUs (1115 of 2243 total OTUs) originated from blood, whereas 36.20% (812 of 2243 total OTUs) and 15.25% OTUs (342 of 2243 total OTUs) were present in the gut and torsaloes, respectively. The specific OTUs only accounted for 34.24% (768 of 2243 total OTUs) of the total flea OTUs. When we randomly extracted a different nine samples, the results were still similar (data not shown). To determine the microbes that may colonize host blood, we calculated the mean relative abundance of shared OTUs between groups. The results showed that most OTUs (>70%) shared between blood and torsalo, flea or gut were present in pika ectoparasites or gut at relatively low abundances of 0.1% or less (Fig. 4B–D, cluster of points near origin). The relative abundance of these OTUs in ectoparasites or gut of pikas was also low in the blood samples.

Differences of gene functions between blood and other sample types A total of 41 gene functions were predicted based on the bacterial communities at level 2. The differences of predicted gene functions between blood and gut, fleas or torsaloes were analyzed (Table S3, Supporting Information). The gene functions associated with human diseases (e.g. cancers, cardiovascular diseases, infectious diseases and neurodegenerative diseases) and metabolism (e.g. glycan biosynthesis and metabolism, lipid

Li et al.

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Table 2. Detection and distribution of potential pathogenic OTUs in blood, fleas, torsaloes and gut, and pathogenicity of the corresponding bacterial genus. Proportion of sequences of OTUs in each group OTU Identification (genus level)

OTU number

Total sequences

Torsaloes

Fleas

blood

gut

Epidemiolology

Fusobacterium

2

2246

0.9831

0.016

0.0009

0

Rickettsia

10

5636

0

0.9968

0.0032

0

Kingella

1

1879

0.9979

0.0005

0.0016

0

Porphyromonas 3

1099

0.9964

0.0009

0

0.0027

Bartonella

76

25 114

0.0001

0.4128

0.5870

0.0001

Mycoplasma

2

396

0

0

0.9975

0.0025

Some strains of Fusobacterium may cause several humans diseases, drome and skin ulcers such as periodontal diseases, Lemierre’s syndrome and skin ulcers (Aliyu et al. 2004). Rickettsia, especially Rickettsia typhi, was transmitted by fleas and other arthropod vectors, and has caused endemic typhus in many countries (Labruna 2009). Kingella has been an important cause of invasive infections in children, such as septic arthritis, osteomyelitis, spondylodiscitis and bacteraemia (Yagupsky 2004). Some strains of Porphyromonas may cause Periodontal diseases (Lamont and Jenkinson 1998). Several rodent-borne Bartonella species may cause various zoonotic diseases, such as fever, bacteremia and neurological diseases (Meerburg, Singleton and Kijlstra 2009). Some members of Mycoplasma may cause anemia (Pitcher and Nicholas 2005).

Table 3. Differences in alpha diversity of bacterial communities from the blood, gut of pikas and their flea and torsalo vectors.

Sample type Torsalo Flea Blood Gut

Observed OTUs

Shannon diversity

Phylogenetic diversity

Chao1

Evenness

114±30a 369±35b 510±77b 719±21c

1.7±0.2a 3.6±0.2b 4.4±0.6b 7.3±0.1c

17.5±3.8a 46.4±3.3b 56.9±7.2bc 59±1.6c

337±98a 1004±72b 1022±136b 1446±57c

0.26±0.03a 0.42±0.02b 0.48±0.05b 0.77±0.01c

Data were expressed as Mean ± SE. Significant difference is marked with different letters between sample types.

Figure 2. PcoA plots of unweighted and weighted UniFrac distances comparing the bacterial communities in the blood, gut, flea and torsalo.

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Table 4. ANOSIM showing different community memberships and structures among blood, gut, fleas and torsaloes. Unweighted UniFrac

Sample type Blood vs Gut Blood vs Flea Blood vs Torsalo Flea vs Torsalo Flea vs Gut Torsalo vs Gut

Weighted UniFrac

R

Sig.

R

Sig.

0.756 0.92 0.387 0.502 0.841 0.859 0.932