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A total infectome approach to understand the etiology of infectious disease in pigs

Abstract

Background

The global pork industry is continuously affected by infectious diseases that can result in large-scale mortality, trade restrictions, and major reductions in production. Nevertheless, the cause of many infectious diseases in pigs remains unclear, largely because commonly used diagnostic tools fail to capture the full diversity of potential pathogens and because pathogen co-infection is common.

Results

We used a meta-transcriptomic approach to systematically characterize the pathogens in 136 clinical cases representing different disease syndromes in pigs, as well as in 12 non-diseased controls. This enabled us to simultaneously determine the diversity, abundance, genomic information, and detailed epidemiological history of a wide range of potential pathogens. We identified 34 species of RNA viruses, nine species of DNA viruses, seven species of bacteria, and three species of fungi, including two novel divergent members of the genus Pneumocystis. While most of these pathogens were only apparent in diseased animals or were at higher abundance in diseased animals than in healthy animals, others were present in healthy controls, suggesting opportunistic infections. Importantly, most of the cases examined here were characterized by co-infection with more than two species of viral, bacterial, or fungal pathogens, some with highly correlated occurrence and abundance levels. Examination of clinical signs and necropsy results in the context of relevant pathogens revealed that a multiple-pathogen model was better associated with the data than a single-pathogen model was.

Conclusions

Our data demonstrate that most of the pig diseases examined were better explained by the presence of multiple rather than single pathogens and that infection with one pathogen can facilitate infection or increase the prevalence/abundance of another. Consequently, it is generally preferable to consider the cause of a disease based on a panel of co-infecting pathogens rather than on individual infectious agents.

Video abstract

Background

Pigs are economically important domestic animals that account for over 30% of meat production worldwide [1]. Despite their importance, the productivity of the global swine industry is continually affected by infectious diseases. Large-scale recurring epidemics caused by pathogens such as African swine fever virus (ASFV) [2], porcine reproductive and respiratory syndrome virus (PRRSV) [3, 4], porcine epidemic diarrhea virus (PEDV) [5, 6], and classical swine fever virus (CSFV) [7, 8] have resulted in massive mortality, trade restrictions, and major reductions in production rate [9]. A wide variety of low virulent pathogens, such as pseudorabies virus (PRV) [10] and porcine parvovirus (PPV) [11], are also present in local swine herds and either cause mild disease manifestations that can result in embryonic death and affect pig growth and meat production or impact disease severity as a co-infecting or opportunistic pathogen, such as porcine circovirus 2 (PCV2) [12, 13].

The recent deployment of pathogen discovery technology has identified several epidemics or local outbreaks of emerging pathogens, such as Reston ebolavirus [14] and swine acute diarrhea syndrome coronavirus [15, 16]. These pathogens most likely originated in one mammalian species (such as bats) and were introduced into pigs via cross-species transmission; thus, they also have the potential to infect humans [17]. To date, more than 50 recognized viral pathogens and 46 bacterial pathogens have been associated with pig diseases [18, 19]. However, while most have been described in independent research studies, little attention has been paid to investigating the simultaneous presence and dynamics of these pathogens in pig populations [13, 20,21,22].

Under field conditions, infectious diseases in pigs are often characterized by multiple occurrences of different pathogens within the same individual (i.e., co-infection) and do not easily fit a “one disease, one pathogen” paradigm [13, 23]. Co-infection is common in swine production systems under intensive and packed production conditions [24]. Viruses such as PRRSV, PCV2, and swine influenza A virus (swine IAV) and bacteria such as Streptococcus suis and Mycoplasma hyopneumoniae are often simultaneously detected in pigs with respiratory symptoms [13, 25,26,27,28] and can result in severe disease [29,30,31,32,33,34,35,36,37,38]. In one study of piglets experiencing diarrhea, an average of five and a maximum of ten virus species were identified [39]. Although it is unclear whether all pathogens present in a sample contribute to disease manifestation, co-infecting pathogens may act synergistically, leading to either disease aggravation [26, 33, 40] or chronic forms of disease [41, 42].

Despite numerous efforts to investigate the diversity and complexity of pathogens in pigs, few studies have attempted a comprehensive characterization of all pathogens and their interactions in pigs [13]. Herein, we used a meta-transcriptomics sequencing approach to reveal all viral, bacterial, and fungal pathogens within individual pigs (i.e., their total infectome) in a systematic manner so that they can be characterized simultaneously in the context of specific disease syndromes. Specifically, we investigated 136 clinical cases of different diseases in pigs and revealed the diversity, abundance, and genomic information of each pathogen type. Using these data, we also investigated their interactions, potential clinical manifestations, and epidemiological impact.

Results

The “total infectome” of diseased pigs

Between March 2018 and July 2019, we investigated 136 cases of swine disease (i.e., the “diseased group”) experienced on pig farms from 15 major swine production provinces across China (Additional file 1). Disease severity varied from mild herd illness and growth retardation to large-scale animal mortality. Disease-relevant tissue samples from these outbreaks, including lung, intestine, lymph nodes, liver, spleen, kidney, and mixed tissues, were collected from deceased animals. For comparison, corresponding tissue samples were collected from 12 non-diseased animals (i.e., the “healthy group”), including six from facilities that raise specific pathogen free (SPF)-grade animals for research purposes. Subsequently, we performed total infectome analysis using an unbiased meta-transcriptomics (i.e., total RNA sequencing) approach to reveal all the viruses, bacteria, and eukaryotic pathogens present in each individual sample.

Pathogens were identified by directly comparing sequencing reads against the non-redundant protein, bacterial genome, and universal Cox1 gene databases and confirmed by genome mapping and qPCR (or RT-qPCR) assays. Here, we only considered (i) known pathogens, (ii) opportunistic pathogens, or (iii) uncharacterized viruses related to pathogens that have the potential to cause diseases in mammals. Overall, 34 species of RNA viral pathogens, 9 species of DNA viral pathogens, 7 species of bacteria and mycoplasma pathogens, and 3 species of fungal pathogens were identified in 109 of the 136 total cases examined in this study (Fig. 1A, Additional file 1). Among these were microbes known to cause acute or severe diseases, such as PRRSV, CSFV, PEDV, M. hyopneumoniae, Haemophilus parasuis, and zoonotic pathogens such as S. suis, PRV, hepatitis E virus (HEV), rotavirus A, and Japanese encephalitis virus (JEV). Although the abundance level of JEV (0.19–0.48 reads per million, RPM) was below our threshold (i.e., 1 RPM) to be officially classified as positive, its presence was confirmed by RT-qPCR (Additional file 2). The vast majority of pathogens identified, such as sapoviruses (five species); astroviruses (six species); parvoviruses (four species); herpesviruses (three species); porcine respirovirus 1; rotaviruses B, C, and H; and Mycoplasma hyorhinis, were likely opportunistic or only associated with mild and subclinical disease in pigs.

Fig. 1
figure 1

Total infectome characterization in 136 clinically diseased pigs. A Heat map showing the prevalence and abundance of pathogens in each of the cases. The pathogens (x-axis) are divided into five different groups—RNA viruses, DNA viruses, bacteria, mycoplasma, and fungi. The cases (y-axis) are divided based on sampling year and locations, including seven regions across China. B Comparisons of pathogen abundance measured by qPCR and meta-transcriptomics approaches. Abundance by RT-qPCR methods (x-axis) is measured by cycle threshold, (i.e., Ct value), while those by meta-transcriptomics (y-axis) are measured by RPM in log10 scale. The comparisons are performed on three representative pathogens: PRRSV (RNA virus), PRV (DNA virus), and Pneumocystis (fungi)

In addition to the well-known swine pathogens described above, we discovered two divergent members of the fungal genus Pneumocystis, associated with pneumonia in mice and humans, with a high prevalence and abundance in the lungs of diseased pigs (Fig. 2). Analyses of the sample (H60-lun) with the highest Pneumocystis abundance revealed two separate lineages that shared 88.48% nucleotide identity with each other and 81.65% and 81.20% identity with their closest relative, Pneumocystis sp. macacae. This divergence suggests that these lineages represent two different species. Based on the widely accepted trinomial nomenclature, we tentatively named these pathogens as Pneumocystis sp. suis 1 and 2 [43]. Phylogenetic analyses revealed a generally consistent topology in the mitochondrial and nuclear genes, in which these two new species formed a monophyletic cluster distinct from that of the Pneumocystis found in other mammalian species (Fig. 2A), consistent with previous findings based on ribosomal RNA genes [44]. Interestingly, the two Pneumocystis species identified here often co-appeared in the samples we examined (9/12) and were mostly associated with lung infection (Fig. 2B), although their corresponding abundance levels sometimes differed. Further characterization of the transcriptomic profiles of these two Pneumocystis spp. revealed the expression of a functionally diverse range of genes associated with energy metabolism, replication, structure, and cell survival (Fig. 2C), confirming that these are metabolically active microbes that exist at high abundance within the diseased host. Finally, the presence of Pneumocystis was confirmed by Grocott’s methenamine silver (GMS) staining, which showed a clustering of dark brown, ovoid Pneumocystis cysts (Fig. 2D) in samples that tested positive for Pneumocystis spp. but not in negative samples (Fig. 2E).

Fig. 2
figure 2

The diversity, prevalence and gene expression profiles of the two Pneumocystis spp. identified in this study. A Evolutionary history of the two Pneumocystis spp. within the context of existing members of genus Pneumocystis. The phylogenetic trees were estimated using a maximum likelihood method on the COI and Actin-1 genes. B Bar graph of the presence and abundance of each type of Pneumocystis identified here. C Gene expression profiles (measured in RPM) for Pneumocystis in sample H60-lun (left panel) and all the other samples (right panel). The presence of Pneumocystis was confirmed by GMS staining of DPneumocystis positive and€) Pneumocystis negative samples

Evaluation of infection status by quantification of pathogen transcriptomes

Marked variation in pathogen abundance, measured in RPM mapped to pathogen genomes (viruses and bacteria) or mitochondrial genomes (fungi), was observed. While we set the lowest threshold for pathogen detection at 1 RPM, the highest abundance reached 28,519 RPM (or 2.85% of total non-ribosomal RNA) and was observed in a lung sample infected with CSFV (Additional file 1). The highest abundance levels (> 105 RPM) were always associated with RNA virus infection, although relatively high abundance levels (> 103 RPM) were also frequently observed for transcripts from DNA viruses, bacteria, and fungi (Additional file 1). For the newly identified fungal Pneumocystis spp., the highest abundance level was 2828 based on mapping against mitochondrial transcripts. To validate the presence and abundance of pathogens, we performed RT-qPCR or qPCR assays for PRRSV (RNA virus), PRV (DNA virus), and Pneumocystis spp. (fungi). The correlation between the RPM value and the threshold cycle (Ct) value estimated by RT-qPCR or qPCR assay was high (Pearson’s r = − 0.82 to − 0.90, Fig. 1B), suggesting that meta-transcriptomics produced a reliable estimate of pathogen abundance.

Comparisons of infectomes in diseased and healthy pigs

To establish the potential disease associations of the pathogens discovered in this study, we compared the prevalence and abundance of each pathogen in diseased and healthy pigs (Fig. 3A). In many cases, including swine IVA, PRRSV, PEDV, porcine hemagglutinating encephalomyelitis virus (PHEV), and PCV2, extremely low levels of abundance, or even complete absence, were observed in the healthy pig group, suggesting that these are bona fide pathogens. Conversely, a small number of viruses appeared in both the diseased and healthy groups: sapoviruses, astroviruses, rotavirus A, and rotavirus C showed similar prevalence and abundance between the two groups, whereas Suid betaherpesvirus 2, parvoviruses, and the newly identified Pneumocystis spp. had much lower prevalence and abundance in healthy pigs than in diseased pigs (Fig. 3A). We also identified the possible organ/tissue tropisms for each pathogen (Fig. 3B). For example, PRRSV and PCV2 were mainly detected in the lungs and lymph nodes, whereas pasivirus A and the newly identified Pneumocystis spp. were strictly limited to the lungs. Nevertheless, some pathogens commonly associated with enteric diseases, namely astroviruses, rotaviruses, and Sapporo virus, also appeared in other organs from both the healthy and diseased groups, although generally at low abundance (Fig. 3B).

Fig. 3
figure 3

Disease association and tissue preference for the pathogens identified in this study. A Comparisons of pathogen abundance between the diseased and control groups. Each boxplot shows the abundance (measured by RPM) distribution of diseased (red) and control (blue) cases. B Bar graph showing the prevalence level of pathogens (y-axis) in different types of diseased and healthy tissues - lung, gut and lymph node

In-depth genomic analysis of swine diseases

Our meta-transcriptomics analyses enabled an in-depth characterization of all the pathogens present in the animals studied here, including detailed genotyping/subtyping, estimation of intra-specific diversity, and identification of temporal-geographic patterns (Fig. 4). Notably, multiple genotypes/subtypes with distinct pathogenicity and transmissibility were identified. For example, both type 1 and 2 PRRSV were detected (Fig. 4A, Additional file 3), and we identified four lineages (lineages 1, 3, 5, and 8) within the type 2 PRRSV (Fig. 4A). While lineage 5 is largely associated with the modified live vaccine Ingelvac PRRS® MLV, the other three lineages have either been endemic in China for over 20 years (lineages 3 and 8) or recently introduced to China (lineage 1) [45]. Among the PRRSV, higher pathogenicity was associated with lineages 1 and 8, often referred to as NADC30-like and HP-PRRSV, respectively, which comprised the majority of the cases (24 out of 33; Fig. 4A). Similarly, more than two genetic variants were identified for each of the pathogens known to be associated with acute or severe diseases, namely PEDV, CSFV, IVA, PCV2, and Getah virus, which were most closely related to viruses identified within China or from neighboring Asian countries (Fig. 4A, Additional file 3), indicative of regionalized transmission. Substantial diversity at both inter-specific and intra-specific levels was also observed in the case of the less pathogenic virus families/genera, such as those from Astroviridae and sapovirus (Caliciviridae) (Fig. 4B). Indeed, we identified more than six species of astrovirus and five species of sapovirus, many of which shared < 90% nucleotide similarity with existing sequences, meriting the designation of new genotypes or subtypes. Among these, porcine astrovirus 4 was more commonly detected than other astrovirus species and was shed by both healthy and diseased swine. Sapovirus genogroup III was the predominant variant detected in the present study. Finally, the viruses identified in healthy pigs were very closely related to those found in diseased animals in the case of rotaviruses, sapoviruses, and astroviruses (Fig. 4 and Additional file 3), again suggesting that these variants/species were unlikely to cause overt diseases on their own.

Fig. 4
figure 4

Phylogenetic diversity of the viruses identified in this study. Species level (A) and family level (B) maximum likelihood phylogenetic trees. Sequences identified from the diseased groups are marked with a red solid circle, whereas those from healthy controls are marked with a blue solid circle. For clarity, sequence names were not shown on the tree. The corresponding taxonomy/lineage and geographic information are provided on the right of the tree

Microbial co-infection and infectome complexity

Microbial co-infections were common in the diseased pigs examined in this study. In the case of viruses, 82 samples were infected with at least two virus species compared with 52 samples infected with only one virus (Fig. 5A). Interestingly, 22 samples were co-infected with more than five viruses. Adding to this complexity were co-infections among viruses, bacteria, and fungi (Fig. 5B), revealing highly complex infectomes for diseased pigs that may not be easily explained by a single pathogen–single disease model.

Fig. 5
figure 5

Co-infection characteristics and associations within the infectome. A Distribution of viral co-infections. B Co-infection frequencies among four different categories of pathogen—RNA virus, DNA virus, bacteria, and fungi. C Co-infection network based on frequencies estimated between each pair of important pathogens. D Correlation of abundance levels among different pathogens. E Boxplot showing the abundance distribution and prevalence (number of dots) comparisons of four viruses—PCV2, CSFV, astroviruses—M. hyorhinis, and Pneumocystis between PRRSV positive and PRRSV negative samples

We further estimated the co-infection frequency of each pair of pathogens. This revealed higher frequencies of co-infection with PRRSV than of co-infection without PRRSV, although PRRSV was the most prevalent pathogen in our dataset (Fig. 5C). Indeed, for a number of respiratory pathogens, such as PCV2, M. hyorhinis, and H. parasuis, the majority of the cases had co-infection with PRRSV. Despite the dominance of PRRSV, viruses commonly associated with the digestive tract—rotaviruses, sapoviruses, and astroviruses—had a higher frequency of co-infection with each other than with PRRSV (Fig. 5C). Furthermore, positive correlations in the abundance (p < 0.05) of these viruses were found, indicating that their infection within the gut sometimes occurred synchronously. For example, in sample S144-gut from a pig experiencing acute digestive symptoms, we identified ten pathogens associated with the digestive tract, four of which were highly abundant: sapporovirus (330 RPM), rotavirus B (1,091 RPM), rotavirus C (354 RPM), and astroviruses (4,127 RPM) (Additional file 1). Relatively high positive correlations (p < 0.05) were also observed between PRRSV and M. hyorhinis, PRRSV and CSFV, and CSFV and H. parasuis (Fig. 5D). Nevertheless, the presence of PRRSV was associated with an increased prevalence and/or abundance of some other pathogens, such as PCV2, CSFV, and M. hyorhinis, although no effect was observed with pathogens such as astroviruses and Pneumocystis spp. (Fig. 5E).

Clinical manifestations

The complexity of infectomes often makes determining the causal relationship between the pathogen and disease challenging. Therefore, we examined the clinical symptoms and necropsy results in the context of a panel of relevant pathogens instead of individual microbes (Fig. 6). Compared with the single-pathogen model (Additional file 4), the infectome model provided more complete information on pathogen–disease relationships (Fig. 6). For respiratory symptoms, PRRSV and a number of co-infecting pathogens such as PCV2 and M. hyorhinis were the main contributors to the disease, although other pathogens such as Pneumocystis spp., pseudorabies virus, and swine H1N1 influenza A viruses were also frequently detected in the PRRSV-negative cases. Conversely, digestive symptoms or an “intestinal wall thinning” syndrome was often characterized by a combination of more than four digestive system-associated pathogens, many at high abundance (Fig. 6). Furthermore, 16 of the 25 cases with PRRSV and CSFV, singly or in combination, were associated with “lymph node enlargement and hemorrhage,” “splenic infarction,” “renal hemorrhage,” or “coagulation disorder.” Five of six cases infected with PCV2 (alone or with PRRSV) were associated with “shiver,” a neurological symptom. Finally, the “abortion” symptoms in our study were seemingly associated with a number of pathogens, among which PRRSV, PCV2, Getah virus, PHEV, and S. suis were present in relatively high abundance.

Fig. 6
figure 6

Pathogens associated with 15 disease types in pigs. For each of the disease types, a heat map displays the prevalence and abundance of the pathogens identified. Disease types include general symptoms (e.g., respiratory symptoms, digestive symptoms), specific symptoms (e.g., shiver, abortion, emaciation), as well as autopsy information (e.g., lung consolidation, kidney hemorrhage)

Discussion

We have revealed total infectomes associated with diseased pigs in China, identifying complex interactions of multiple infectious agents within which co-infections are common [13, 23]. Although sequencing data alone is unlikely to reveal the clinical consequences and fine mechanisms for every pathogen combination, we examined potential models of pathogen interaction. First, we considered the single-pathogen model, which assumes that one major pathogen contributes to most disease manifestations and that the remaining pathogens having a negligible effect on the main clinical symptoms. This was the case when the pigs were infected with highly pathogenic viruses, such as ASFV, and we expected to observe the dominance of the causative agent over other pathogens. For example, case JL89, presenting with severe digestive symptoms, was infected with PEDV (9,230 RPM) alone. However, the single-pathogen model is relatively uncommon in our data, and most cases were characterized by the presence of other pathogens that are likely relevant to disease manifestation. For example, the case of S023, in which the animal experienced abortion, was characterized by a high frequency of Getah virus in the lungs (18,672 RPM); however, PEDV was also present (3 RPM), suggesting that its clinical symptoms might have reflected virus co-infection. Furthermore, even for highly virulent pathogens such as CSFV (12,267–28,517 RPM), disease-relevant co-pathogens such as PRRSV (2549–7392 RPM) and H. parasuis (737 RPM) often appeared within individual animals, again suggesting that the single-pathogen model is too simplistic.

As most cases (88/136) were associated with at least two relevant pathogens, our data favor a model in which the interaction among co-infecting pathogens may contribute to disease manifestation in pigs, although these pathogens can independently induce disease. Indeed, it has been demonstrated under both experimental and field conditions that infections with two or more disease-relevant pathogens, including virus–virus [13, 20, 21, 29, 33, 35, 37, 38, 41, 42, 46,47,48,49,50], virus–bacteria [13, 23, 31, 32, 34, 36, 51,52,53,54,55,56,57,58,59,60], and bacteria–bacteria [13, 30, 61,62,63] co-infections, can result in changes in pathogen replication, more severe clinical outcomes, or longer disease duration than that of individual infections alone. The mechanism of increased pathogenicity is complex and largely depends on the specific pathogens involved [13, 23]. Generally, an impaired host immune system, damaged epithelial barrier, and excessive inflammatory response can result in complex infection scenarios and worse clinical outcomes [23].

One of the most important co-infecting pathogens in pigs is PRRSV. This virus was not only ubiquitous in diseased pigs but also associated with an increase in the prevalence and/or abundance of other viral and bacterial pathogens, such as PCV2 and M. hyorhinis (Fig. 5E); in this study, M. hyorhinis was identified in ten pigs, all of which contained PRRSV as a co-pathogen. Indeed, co-infections involving PRRSV are well documented, and PRRSV, which targets macrophages and dendritic cells in the respiratory tract, is generally believed to be able to impair the host immune system, resulting in increased susceptibility to other pathogens [64,65,66].

Another interesting observation was that many pathogens associated with enteric diseases, particularly astroviruses, Sapporo virus, picornaviruses, and rotaviruses, also appeared in healthy controls, even in SPF pigs, at relatively high abundance levels (> 100 RPM). Since the enteric diseases described here as well as in previous studies were usually characterized by concurrent infections with multiple pathogens, these enteric pathogens may act in a synergistic or additive manner such that only multiple pathogens can lead to overt or severe enteric symptoms [67]. This is supported by the simultaneous occurrence and high abundance correlation of astroviruses, sapoviruses, picornaviruses, and rotaviruses in the diseased samples (Fig. 5). As these co-infecting pathogens usually show similar clinical signs, achieving an accurate diagnosis based on clinical presentation alone is often difficult.

We identified two divergent species in the genus Pneumocystis that may be potential respiratory fungal pathogens in pigs. Pneumocystis has been identified in a variety of mammalian species, including humans, and is usually an opportunistic infection of the lungs. Pneumocystis infections have been frequently detected in diseased and healthy pigs, and surveys conducted in Denmark, Japan, Korea, and Brazil revealed a 7–60% positive rate [44, 68,69,70,71]. While a few studies have suggested a lack of association with pulmonary lesions [44, 72], others have revealed an important link to disease outbreaks [69, 73]. Although Pneumocystis was present in both healthy and diseased samples of the pigs studied here, it was at a significantly elevated abundance in diseased animals. Furthermore, Pneumocystis was identified with other pathogens, including PRRSV (n = 10), pasivirus A (n = 5), and PCV2 (n = 4), suggesting that Pneumocystis infection may follow primary viral infections in these cases. While its disease manifestation requires further investigation, the Pneumocystis identified in pigs is highly divergent from Pneumocystis carinii, requiring a new species designation.

Notably, we also identified several zoonotic pathogens that pose a potential threat to human health. Among these, S. suis, which causes sporadic human outbreaks in Asian countries, with a mortality rate as high as 19.2% [74], was identified in 7/136 cases presented here and in multiple Chinese provinces. In addition, we detected a number of vector-borne viruses (i.e., JEV and Zika virus) for which pigs may act as intermediate or amplifying hosts. Interestingly, a latent virus present at high prevalence in the pig population, PRV, is now associated with human endophthalmitis [75]. In addition, we identified porcine respirovirus 1, which shared the highest relative (77.1% identity) to human parainfluenza virus 1 [76]. Since porcine organs are considered favorable resources for xenotransplantation [77], a particular concern is that viruses characterized by both ubiquitous presence and latent infection can result in disease manifestations after transplantation. Indeed, some opportunistic pathogens were present in SPF pigs. Hence, a thorough pathogen investigation is required to exclude any potential threats to organ recipients.

Our study highlights the power of meta-transcriptomics to understand the etiological basis of infectious diseases in pigs. Importantly, this approach can (i) reveal all types of pathogens in a single assay, showing that multiple infections are the norm rather than the exception; (ii) provide accurate estimations of pathogen abundance; and (iii) help identify each pathogen at the most precise taxonomic level using the genomic sequences obtained, which enables scrutiny of detailed epidemiological history. Collectively, the total infectome revealed by meta-transcriptomics represents a powerful model for understanding infectious diseases in domestic animals, which goes beyond pathogen discovery and characterization.

Conclusions

Here, we describe the total infectome associated with diseased pigs in China, revealing a huge diversity of viruses, bacteria, and eukaryotic pathogens. Although most of these are well-known swine pathogens, we identified two divergent species in the genus Pneumocystis associated with respiratory symptom in pigs. More importantly, our findings revealed that most of the pig diseases examined were better explained by the presence of multiple rather than single pathogens and that infection with one pathogen can facilitate infection or increase the prevalence/abundance of another pathogen, resulting in more complicated and severe clinical manifestations in diseased pigs. Therefore, it is generally preferable to consider the cause of a disease based on a panel of co-infecting pathogens rather than individual infectious agents. In summary, our study highlights the complexity of the infectome in each pig disease syndrome and underlines the importance of performing comprehensive pathogen characterization before a diagnosis is made.

Methods

Sample collection

A total of 185 tissue samples from 136 diseased pigs were collected in China between March 2018 and July 2019 (Additional file 1). The sample collection covered 15 provinces, especially those with major swine industries, including Shandong, Guangdong, Hebei, and Heilongjiang. For comparison, six SPF-grade and six clinically healthy piglets were collected from Heilongjiang Province. The SPF piglets, which were provided by the National Science and Technology Infrastructure Centre (Harbin), were free of 23 main pathogens, which was confirmed using RT-qPCR and/or ELISA. These pathogens include ten viruses, namely foot-and-mouth disease virus (serotypes A and O), transmissible gastroenteritis virus, PCV2, PRRSV, PRV, CSFV, PEDV, JEV, swine IAV, and ASFV; ten bacteria, namely Pasteurella multocida, Bordetella bronchiseptica, M. hyopneumoniae, Actinobacillus pleuropneumoniae, Brucella, Brachyspira hyodysenteriae, Salmonella, H. parasuis, S. suis, and Leptospira; and three parasites, Toxoplasma gondii, lice, and Sarcoptes scabiei. Tissue samples from the lung, spleen, intestine, liver, kidney, and lymph nodes were collected from both diseased and healthy pigs. The tissues were first processed to remove irrelevant connective tissue and blood vessels. A small piece of tissue (< 0.5 cm) was subsequently cut from the bottom and immediately immersed in RNAlater™ (Invitrogen, Waltham, MA, USA) to prevent RNA degradation. All samples were transported on dry ice and stored at − 80 °C until further processing.

Meta-transcriptomic sequencing

After thawing on ice, the tissues were rinsed with sterile phosphate-buffered saline (PBS) to remove blood and contaminants from the surface. Subsequently, a piece of tissue was cut from the bottom of the sample and homogenized in PBS. Total RNA was extracted from the homogenate using TRIzol (Invitrogen), followed by ribosomal RNA (rRNA) removal using a RiboZero Gold Kit (Human/Mouse/Rat) and Trueseq (Illumina, San Diego, CA, USA) RNA library construction. The libraries were then subjected to 150 bp pair-end sequencing on the Illumina Hiseq 4000 or NovaSeq platform (Illumina).

Pathogen discovery and characterization

The sequencing results were subjected to quality control procedures, including the removal of low-quality reads, adaptor sequences, non-complex reads, and duplicated reads, using the BBmap software package (https://sourceforge.net/projects/bbmap/). Pig rRNA reads were subsequently removed by mapping against a comprehensive rRNA sequence collection downloaded from the SILVA database (http://www.arb-silva.de) [78]. The remaining reads were either (i) directly compared against the non-redundant protein (nr) database using Diamond Blastx [79], with an e value threshold set at 1E-5, or (ii) assembled into contigs using Megahit [80] before comparisons against the nr database.

For virus identification, taxonomic information was obtained for each of the blast hits, and those that matched the kingdom “Viruses” were retained. False-positives and endogenous virus elements were identified and subsequently removed by comparing the reads and contigs against the non-redundant nucleotide (nt), whole genome shotgun, and vector sequence databases. Virus contigs/genomes with < 90% amino acid similarity to known viruses were treated as potential novel virus species. For bacterial and fungal identification, we first used MetaPhlAn2 [81] to identify potential pathogens in both groups. Relevant background bacterial and fungal mitochondrial genomes were downloaded from NCBI/GenBank and used as templates for read mapping and abundance estimation. Based on the mapping results for each case, we generated relevant contigs (i.e., consensus sequences) for Blastn comparisons against the nt database to determine microbial taxonomy at the species level. Highly divergent fungal species were first identified based on Blastx analyses against the nr database and then confirmed by comparing key genes (e.g., COI and Actin-1) against sequences from related taxa.

The abundance level for each virus genome was calculated using the following formula: total viral reads/total non-redundant reads × 1,000,000 (i.e., RPM). The abundance levels of bacterial and fungal pathogens were calculated as RPM based on mapped reads against the relevant bacterial and fungal mitochondrial genomes, respectively. A pathogen was considered as “positive” in a sample if its abundance level was greater than 1 RPM. To identify potential false-positives resulting from index hopping, we used a threshold of 0.1% for pathogens present in the libraries from the same sequencing lane: any read numbers < 0.1% of the most abundant library were treated as “negative.”

Virus genome confirmation using RT-qPCR and nested RT-PCR assays

Genomic sequences were confirmed by mapping reads against assembled contigs for each mammalian-associated virus identified in this study. RT-qPCR assay confirmation was performed on representative RNA viruses (i.e., PRRSV) [82] or qPCR for DNA viruses (PRV) [83] and fungi (Pneumocystis) [84] (Additional file 5). The same sample RNA used for meta-transcriptomic analysis was also subjected to primers designed for a specific or related group of pathogens. Accordingly, 1 μg of total RNA was used for reverse transcription (RT). First-strand cDNA was synthesized using M-MLV (TaKaRa, Dalian, China) according to the manufacturer’s instructions and then amplified using Premix Ex Taq (TaKaRa). A Ct value of less than 38 was considered positive. For pathogens with extremely low abundance levels (RPM < 1), namely JEV, a RT-qPCR assay was performed to confirm their presence within the sample [85].

Evolutionary analyses

Pathogen genomes/genes were first aligned with related reference virus sequences downloaded from NCBI/GenBank using the progressive FFT-NS-i algorithm implemented in the MAFFT multiple sequence alignment program [86]. Ambiguously aligned regions were removed using the TrimAl program [87]. Maximum likelihood phylogenetic trees were estimated using PhyML [88], employing the GTR model of nucleotide substitution and SPR branch swapping in all cases. Support for individual nodes in the tree topology was estimated with an approximate likelihood ratio test using Shimodaira–Hasegawa-like procedures.

Grocott’s methenamine silver staining

To confirm the presence of the newly discovered fungi Pneumocystis spp., qPCR-positive lung samples were fixed in 10% buffered formalin, embedded in paraffin, sectioned to 4 μm, and stained with a commercial GMS staining kit (Baso Biotech Ltd, Zhuhai, China) according to the manufacturer’s instructions.

Data availability

All sequencing reads were deposited in the SRA database under the project accession number PRJNA800593. Relevant pathogen genome/gene sequences have been deposited in GenBank under accession numbers OM201171-OM201230, OM151337, and OM149829-OM149830.

Availability of data and materials

All data generated or analyzed during this study are included in the supplementary information files.

Abbreviations

ASFV:

African swine fever virus;

PRRSV:

Porcine reproductive and respiratory syndrome virus

CSFV:

Classical swine fever virus

PEDV:

Porcine epidemic diarrhea virus

PRV:

Pseudorabies virus

PPV:

Porcine parvovirus

PCV2:

Porcine circovirus 2

swine IAV:

Swine influenza A virus

SPF:

Specific pathogen free

HEV:

Hepatitis E virus

RVA:

Rotavirus A

JEV:

Japanese encephalitis virus

GMS:

Grocott’s methenamine silver

PHEV:

Porcine hemagglutinating encephalomyelitis virus

RPM:

Reads per million

aLRT:

Approximate likelihood ratio test

References

  1. FAO. Food Outlook – Biannual Report on Global Food Market: FAO; 2020.

    Google Scholar 

  2. Zhou X, Li N, Luo Y, Liu Y, Miao F, Chen T, et al. Emergence of African swine fever in China, 2018. Transbound Emerg Dis. 2018;65:1482–4.

    PubMed  Article  Google Scholar 

  3. Tian K, Yu X, Zhao T, Feng Y, Cao Z, Wang C, et al. Emergence of fatal PRRSV variants: unparalleled outbreaks of atypical PRRS in China and molecular dissection of the unique hallmark. PLoS One. 2007;2:e526.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. Zhao K, Ye C, Chang XB, Jiang CG, Wang SJ, Cai XH, et al. Importation and recombination are responsible for the latest emergence of highly pathogenic porcine reproductive and respiratory syndrome virus in China. J Virol. 2015;89:10712–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Li W, Li H, Liu Y, Pan Y, Deng F, Song Y, et al. New variants of porcine epidemic diarrhea virus, China, 2011. Emerg Infect Dis. 2012;18:1350–3.

    PubMed  PubMed Central  Article  Google Scholar 

  6. Wang L, Byrum B, Zhang Y. New variant of porcine epidemic diarrhea virus, United States, 2014. Emerg Infect Dis. 2014;20:917–9.

    PubMed  PubMed Central  Article  Google Scholar 

  7. Luo Y, Li S, Sun Y, Qiu HJ. Classical swine fever in China: a minireview. Vet Microbiol. 2014;172:1–6.

    PubMed  Article  Google Scholar 

  8. Zhou B. Classical Swine Fever in China-An Update Minireview. Front Vet Sci. 2019;6:187.

    PubMed  PubMed Central  Article  Google Scholar 

  9. VanderWaal K, Deen J. Global trends in infectious diseases of swine. Proc Natl Acad Sci U S A. 2018;115:11495–500.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Pomeranz LE, Reynolds AE, Hengartner CJ. Molecular biology of pseudorabies virus: impact on neurovirology and veterinary medicine. Microbiol Mol Biol Rev. 2005;69:462–500.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Streck AF, Truyen U. Porcine Parvovirus. Curr Issues Mol Biol. 2020;37:33–46.

    PubMed  Article  Google Scholar 

  12. Chae C. A review of porcine circovirus 2-associated syndromes and diseases. Vet J. 2005;169:326–36.

    CAS  PubMed  Article  Google Scholar 

  13. Saade G, Deblanc C, Bougon J, Marois-Créhan C, Fablet C, Auray G, et al. Coinfections and their molecular consequences in the porcine respiratory tract. Vet Res. 2020;51:80.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Barrette RW, Metwally SA, Rowland JM, Xu L, Zaki SR, Nichol ST, et al. Discovery of swine as a host for the Reston ebolavirus. Science. 2009;325:204–6.

    CAS  PubMed  Article  Google Scholar 

  15. Gong L, Li J, Zhou Q, Xu Z, Chen L, Zhang Y, et al. A New Bat-HKU2-like Coronavirus in Swine, China, 2017. Emerg Infect Dis. 2017;23:1607–9.

    CAS  PubMed Central  Article  Google Scholar 

  16. Zhou P, Fan H, Lan T, Yang XL, Shi WF, Zhang W, et al. Fatal swine acute diarrhoea syndrome caused by an HKU2-related coronavirus of bat origin. Nature. 2018;556:255–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Lun ZR, Wang QP, Chen XG, Li AX, Zhu XQ. Streptococcus suis: an emerging zoonotic pathogen. Lancet Infect Dis. 2007;7:201–9.

    PubMed  Article  Google Scholar 

  18. Swine Health Information Center. 2018. https://www.swinehealth.org. Accessed 2 Mar 2021.

  19. Hause BM, Padmanabhan A, Pedersen K, Gidlewski T. Feral swine virome is dominated by single-stranded DNA viruses and contains a novel Orthopneumovirus which circulates both in feral and domestic swine. J Gen Virol. 2016;97:2090–5.

    PubMed  Article  CAS  Google Scholar 

  20. Choi YK, Goyal SM, Joo HS. Retrospective analysis of etiologic agents associated with respiratory diseases in pigs. Can Vet J. 2003;44:735–7.

    PubMed  PubMed Central  Google Scholar 

  21. Fablet C, Marois-Créhan C, Simon G, Grasland B, Jestin A, Kobisch M, et al. Infectious agents associated with respiratory diseases in 125 farrow-to-finish pig herds: a cross-sectional study. Vet Microbiol. 2012;157:152–63.

    CAS  PubMed  Article  Google Scholar 

  22. Fablet C, Marois C, Kuntz-Simon G, Rose N, Dorenlor V, Eono F, et al. Longitudinal study of respiratory infection patterns of breeding sows in five farrow-to-finish herds. Vet Microbiol. 2011;147:329–39.

    CAS  PubMed  Article  Google Scholar 

  23. Opriessnig T, Giménez-Lirola LG, Halbur PG. Polymicrobial respiratory disease in pigs. Anim Health Res Rev. 2011;12:133–48.

    CAS  PubMed  Article  Google Scholar 

  24. Salogni C, Lazzaro M, Giovannini S, Vitale N, Boniotti MB, Pozzi P, et al. Causes of swine polyserositis in a high-density breeding area in Italy. J Vet Diagn Invest. 2020;32:594–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Blomström AL, Belák S, Fossum C, Fuxler L, Wallgren P, Berg M. Studies of porcine circovirus type 2, porcine boca-like virus and torque teno virus indicate the presence of multiple viral infections in postweaning multisystemic wasting syndrome pigs. Virus Res. 2010;152:59–64.

    PubMed  Article  CAS  Google Scholar 

  26. Chen N, Huang Y, Ye M, Li S, Xiao Y, Cui B, et al. Co-infection status of classical swine fever virus (CSFV), porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circoviruses (PCV2 and PCV3) in eight regions of China from 2016 to 2018. Infect Genet Evol. 2019;68:127–35.

    PubMed  Article  Google Scholar 

  27. Grau-Roma L, Segalés J. Detection of porcine reproductive and respiratory syndrome virus, porcine circovirus type 2, swine influenza virus and Aujeszky's disease virus in cases of porcine proliferative and necrotizing pneumonia (PNP) in Spain. Vet Microbiol. 2007;119:144–51.

    CAS  PubMed  Article  Google Scholar 

  28. Yang Y, Shi R, She R, Mao J, Zhao Y, Du F, et al. Fatal disease associated with Swine Hepatitis E virus and Porcine circovirus 2 co-infection in four weaned pigs in China. BMC Vet Res. 2015;11:77.

    PubMed  PubMed Central  Article  Google Scholar 

  29. Allan GM, McNeilly F, Ellis J, Krakowka S, Meehan B, McNair I, et al. Experimental infection of colostrum deprived piglets with porcine circovirus 2 (PCV2) and porcine reproductive and respiratory syndrome virus (PRRSV) potentiates PCV2 replication. Arch Virol. 2000;145:2421–9.

    CAS  PubMed  Article  Google Scholar 

  30. Ciprián A, Pijoan C, Cruz T, Camacho J, Tórtora J, Colmenares G, et al. Mycoplasma hyopneumoniae increases the susceptibility of pigs to experimental Pasteurella multocida pneumonia. Can J Vet Res. 1988;52:434–8.

    PubMed  PubMed Central  Google Scholar 

  31. Fuentes MC, Pijoan C. Pneumonia in pigs induced by intranasal challenge exposure with pseudorabies virus and Pasteurella multocida. Am J Vet Res. 1987;48:1446–8.

    CAS  PubMed  Google Scholar 

  32. Galina L, Pijoan C, Sitjar M, Christianson WT, Rossow K, Collins JE. Interaction between Streptococcus suis serotype 2 and porcine reproductive and respiratory syndrome virus in specific pathogen-free piglets. Vet Rec. 1994;134:60–4.

    CAS  PubMed  Article  Google Scholar 

  33. Jung K, Renukaradhya GJ, Alekseev KP, Fang Y, Tang Y, Saif LJ. Porcine reproductive and respiratory syndrome virus modifies innate immunity and alters disease outcome in pigs subsequently infected with porcine respiratory coronavirus: implications for respiratory viral co-infections. J Gen Virol. 2009;90:2713–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Shibata I, Okada M, Urono K, Samegai Y, Ono M, Sakano T, et al. Experimental dual infection of cesarean-derived, colostrum-deprived pigs with Mycoplasma hyopneumoniae and pseudorabies virus. J Vet Med Sci. 1998;60:295–300.

    CAS  PubMed  Article  Google Scholar 

  35. Shibata I, Yazawa S, Ono M, Okuda Y. Experimental dual infection of specific pathogen-free pigs with porcine reproductive and respiratory syndrome virus and pseudorabies virus. J Vet Med B Infect Dis Vet Public Health. 2003;50:14–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Thacker EL, Halbur PG, Ross RF, Thanawongnuwech R, Thacker BJ. Mycoplasma hyopneumoniae potentiation of porcine reproductive and respiratory syndrome virus-induced pneumonia. J Clin Microbiol. 1999;37:620–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Van Reeth K, Nauwynck H, Pensaert M. Dual infections of feeder pigs with porcine reproductive and respiratory syndrome virus followed by porcine respiratory coronavirus or swine influenza virus: a clinical and virological study. Vet Microbiol. 1996;48:325–35.

    PubMed  PubMed Central  Article  Google Scholar 

  38. Van Reeth K, Pensaert MB. Porcine respiratory coronavirus-mediated interference against influenza virus replication in the respiratory tract of feeder pigs. Am J Vet Res. 1994;55:1275–81.

    PubMed  Google Scholar 

  39. Zhang B, Tang C, Yue H, Ren Y, Song Z. Viral metagenomics analysis demonstrates the diversity of viral flora in piglet diarrhoeic faeces in China. J Gen Virol. 2014;95:1603–11.

    CAS  PubMed  Article  Google Scholar 

  40. Yu J, Wu J, Zhang Y, Guo L, Cong X, Du Y, et al. Concurrent highly pathogenic porcine reproductive and respiratory syndrome virus infection accelerates Haemophilus parasuis infection in conventional pigs. Vet Microbiol. 2012;158:316–21.

    PubMed  Article  Google Scholar 

  41. Salines M, Barnaud E, Andraud M, Eono F, Renson P, Bourry O, et al. Hepatitis E virus chronic infection of swine co-infected with Porcine Reproductive and Respiratory Syndrome Virus. Vet Res. 2015;46:55.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. Salines M, Dumarest M, Andraud M, Mahé S, Barnaud E, Cineux M, et al. Natural viral co-infections in pig herds affect hepatitis E virus (HEV) infection dynamics and increase the risk of contaminated livers at slaughter. Transbound Emerg Dis. 2019;66:1930–45.

    CAS  PubMed  Article  Google Scholar 

  43. Chabé M, Aliouat-Denis CM, Delhaes L, Aliouat el M, Viscogliosi E, Dei-Cas E. Pneumocystis: from a doubtful unique entity to a group of highly diversified fungal species. FEMS Yeast Res. 2011;11:2–17.

    PubMed  Article  CAS  Google Scholar 

  44. Esgalhado R, Esteves F, Antunes F, Matos O. Study of the epidemiology of Pneumocystis carinii f. sp. suis in abattoir swine in Portugal. Med Mycol. 2013;51:66–71.

    PubMed  Article  Google Scholar 

  45. Guo Z, Chen XX, Li R, Qiao S, Zhang G. The prevalent status and genetic diversity of porcine reproductive and respiratory syndrome virus in China: a molecular epidemiological perspective. Virol J. 2018;15:2.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. Harms PA, Sorden SD, Halbur PG, Bolin SR, Lager KM, Morozov I, et al. Experimental reproduction of severe disease in CD/CD pigs concurrently infected with type 2 porcine circovirus and porcine reproductive and respiratory syndrome virus. Vet Pathol. 2001;38:528–39.

    CAS  PubMed  Article  Google Scholar 

  47. Liu X, Chen L, Song Q, Yang F, Li Y, Zuo Y, et al. Coinfection effects of porcine circovirus type 2 and porcine parvovirus in vivo on phagocytosis and interferon mRNA expression of porcine alveolar macrophages. Wei Sheng Wu Xue Bao. 2011;51:105–14.

    CAS  PubMed  Google Scholar 

  48. Rovira A, Balasch M, Segalés J, García L, Plana-Durán J, Rosell C, et al. Experimental inoculation of conventional pigs with porcine reproductive and respiratory syndrome virus and porcine circovirus 2. J Virol. 2002;76:3232–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Saade G, Ménard D, Hervet C, Renson P, Hue E, Zhu J, et al. Porcine reproductive and respiratory syndrome virus interferes with Swine influenza A virus infection of epithelial cells. Vaccines (Basel). 2020;8:508.

    CAS  PubMed Central  Article  Google Scholar 

  50. Renson P, Deblanc C, Bougon J, Le Dimna M, Gorin S, Mahé S, et al. Concomitant Swine influenza A virus infection alters PRRSV1 MLV Viremia in piglets but does not interfere with vaccine protection in experimental conditions. Vaccines (Basel). 2021;9:356.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Brockmeier SL, Palmer MV, Bolin SR. Effects of intranasal inoculation of porcine reproductive and respiratory syndrome virus, Bordetella bronchiseptica, or a combination of both organisms in pigs. Am J Vet Res. 2000;61:892–9.

    CAS  PubMed  Article  Google Scholar 

  52. Brockmeier SL, Palmer MV, Bolin SR, Rimler RB. Effects of intranasal inoculation with Bordetella bronchiseptica, porcine reproductive and respiratory syndrome virus, or a combination of both organisms on subsequent infection with Pasteurella multocida in pigs. Am J Vet Res. 2001;62:521–5.

    CAS  PubMed  Article  Google Scholar 

  53. Cho JG, Dee SA, Deen J, Trincado C, Fano E, Jiang Y, et al. The impact of animal age, bacterial coinfection, and isolate pathogenicity on the shedding of porcine reproductive and respiratory syndrome virus in aerosols from experimentally infected pigs. Can J Vet Res. 2006;70:297–301.

    PubMed  PubMed Central  Google Scholar 

  54. Loving CL, Brockmeier SL, Vincent AL, Palmer MV, Sacco RE, Nicholson TL. Influenza virus coinfection with Bordetella bronchiseptica enhances bacterial colonization and host responses exacerbating pulmonary lesions. Microb Pathog. 2010;49:237–45.

    CAS  PubMed  Article  Google Scholar 

  55. Opriessnig T, Thacker EL, Yu S, Fenaux M, Meng XJ, Halbur PG. Experimental reproduction of postweaning multisystemic wasting syndrome in pigs by dual infection with Mycoplasma hyopneumoniae and porcine circovirus type 2. Vet Pathol. 2004;41:624–40.

    CAS  PubMed  Article  Google Scholar 

  56. Thacker EL, Thacker BJ, Janke BH. Interaction between Mycoplasma hyopneumoniae and swine influenza virus. J Clin Microbiol. 2001;39:2525–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Thanawongnuwech R, Brown GB, Halbur PG, Roth JA, Royer RL, Thacker BJ. Pathogenesis of porcine reproductive and respiratory syndrome virus-induced increase in susceptibility to Streptococcus suis infection. Vet Pathol. 2000;37:143–52.

    CAS  PubMed  Article  Google Scholar 

  58. Thanawongnuwech R, Thacker B, Halbur P, Thacker EL. Increased production of proinflammatory cytokines following infection with porcine reproductive and respiratory syndrome virus and Mycoplasma hyopneumoniae. Clin Diagn Lab Immunol. 2004;11:901–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Thanawongnuwech R, Young TF, Thacker BJ, Thacker EL. Differential production of proinflammatory cytokines: in vitro PRRSV and Mycoplasma hyopneumoniae co-infection model. Vet Immunol Immunopathol. 2001;79:115–27.

    CAS  PubMed  Article  Google Scholar 

  60. Xu M, Wang S, Li L, Lei L, Liu Y, Shi W, et al. Secondary infection with Streptococcus suis serotype 7 increases the virulence of highly pathogenic porcine reproductive and respiratory syndrome virus in pigs. Virol J. 2010;7:184.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. Brockmeier SL. Prior infection with Bordetella bronchiseptica increases nasal colonization by Haemophilus parasuis in swine. Vet Microbiol. 2004;99:75–8.

    PubMed  Article  Google Scholar 

  62. Brockmeier SL, Register KB. Expression of the dermonecrotic toxin by Bordetella bronchiseptica is not necessary for predisposing to infection with toxigenic Pasteurella multocida. Vet Microbiol. 2007;125:284–9.

    CAS  PubMed  Article  Google Scholar 

  63. Marois C, Gottschalk M, Morvan H, Fablet C, Madec F, Kobisch M. Experimental infection of SPF pigs with Actinobacillus pleuropneumoniae serotype 9 alone or in association with Mycoplasma hyopneumoniae. Vet Microbiol. 2009;135:283–91.

    CAS  PubMed  Article  Google Scholar 

  64. Thanawongnuwech R, Thacker EL, Halbur PG. Effect of porcine reproductive and respiratory syndrome virus (PRRSV) (isolate ATCC VR-2385) infection on bactericidal activity of porcine pulmonary intravascular macrophages (PIMs): in vitro comparisons with pulmonary alveolar macrophages (PAMs). Vet Immunol Immunopathol. 1997;59:323–35.

    CAS  PubMed  Article  Google Scholar 

  65. Thanawongnuwech R, Thacker EL, Halbur PG. Influence of pig age on virus titer and bactericidal activity of porcine reproductive and respiratory syndrome virus (PRRSV)-infected pulmonary intravascular macrophages (PIMs). Vet Microbiol. 1998;63:177–87.

    CAS  PubMed  Article  Google Scholar 

  66. Wills RW, Doster AR, Galeota JA, Sur JH, Osorio FA. Duration of infection and proportion of pigs persistently infected with porcine reproductive and respiratory syndrome virus. J Clin Microbiol. 2003;41:58–62.

    PubMed  PubMed Central  Article  Google Scholar 

  67. Saif LJ. Comparative pathogenesis of enteric viral infections of swine. Adv Exp Med Biol. 1999;473:47–59.

    CAS  PubMed  Article  Google Scholar 

  68. Kim KS, Jung JY, Kim JH, Kang SC, Hwang EK, Park BK, et al. Epidemiological characteristics of pulmonary pneumocystosis and concurrent infections in pigs in Jeju Island. Korea. J Vet Sci. 2011;12:15–9.

    PubMed  Article  Google Scholar 

  69. Sanches EM, Pescador C, Rozza D, Spanamberg A, Borba MR, Ravazzolo AP, et al. Detection of Pneumocystis spp. in lung samples from pigs in Brazil. Med Mycol. 2007;45:395–9.

    PubMed  Article  Google Scholar 

  70. Settnes OP, Henriksen SA. Pneumocystis carinii in large domestic animals in Denmark. A preliminary report. Acta Vet Scand. 1989;30:437–40.

    CAS  PubMed  Article  Google Scholar 

  71. Shimizu A, Kimura F, Kimura S. Occurrence of Pneumocystis carinii in animals in Japan. Nihon Juigaku Zasshi. 1985;47:309–11.

    CAS  PubMed  Article  Google Scholar 

  72. Cavallini Sanches EM, Borba MR, Spanamberg A, Pescador C, Corbellini LG, Ravazzolo AP, et al. Co-infection of Pneumocystis carinii f. sp. suis and porcine circovirus-2 (PCV2) in pig lungs obtained from slaughterhouses in southern and midwestern regions of Brazil. J Eukaryot Microbiol. 2006;53(Suppl 1):S92–4.

    PubMed  Article  CAS  Google Scholar 

  73. Kondo H, Taguchi M, Abe N, Nogami Y, Yoshioka H, Ito M. Pathological changes in epidemic porcine Pneumocystis carinii pneumonia. J Comp Pathol. 1993;108:261–8.

    CAS  PubMed  Article  Google Scholar 

  74. Tang J, Wang C, Feng Y, Yang W, Song H, Chen Z, et al. Streptococcal toxic shock syndrome caused by Streptococcus suis serotype 2. PLoS Med. 2006;3:e151.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. Ai JW, Weng SS, Cheng Q, Cui P, Li YJ, Wu HL, et al. Human endophthalmitis caused by pseudorabies virus infection, China, 2017. Emerg Infect Dis. 2018;24:1087–90.

    PubMed  PubMed Central  Article  Google Scholar 

  76. Lau SKP, Woo PCY, Wu Y, Wong AYP, Wong BHL, Lau CCY, et al. Identification and characterization of a novel paramyxovirus, porcine parainfluenza virus 1, from deceased pigs. J Gen Virol. 2013;94:2184–90.

    CAS  PubMed  Article  Google Scholar 

  77. Yang L, Güell M, Niu D, George H, Lesha E, Grishin D, et al. Genome-wide inactivation of porcine endogenous retroviruses (PERVs). Science. 2015;350:1101–4.

    CAS  PubMed  Article  Google Scholar 

  78. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    CAS  PubMed  Article  Google Scholar 

  79. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.

    CAS  PubMed  Article  Google Scholar 

  80. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.

    CAS  PubMed  Article  Google Scholar 

  81. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12:902–3.

    CAS  PubMed  Article  Google Scholar 

  82. Chen N, Ye M, Xiao Y, Li S, Huang Y, Li X, et al. Development of universal and quadruplex real-time RT-PCR assays for simultaneous detection and differentiation of porcine reproductive and respiratory syndrome viruses. Transbound Emerg Dis. 2019;66:2271–8.

    CAS  PubMed  Article  Google Scholar 

  83. Yoon HA, Eo SK, Aleyas AG, Cha SY, Lee JH, Chae JS, et al. Investigation of pseudorabies virus latency in nervous tissues of seropositive pigs exposed to field strain. J Vet Med Sci. 2006;68:143–8.

    CAS  PubMed  Article  Google Scholar 

  84. Weissenbacher-Lang C, Nedorost N, Knecht C, Hennig-Pauka I, Weissenböck H. Establishment of a quantitative real-time PCR for the detection of Pneumocystis carinii f. sp. suis in bronchoalveolar lavage samples from pigs. J Vet Diagn Invest. 2016;28:257–62.

    CAS  PubMed  Article  Google Scholar 

  85. Santhosh SR, Parida MM, Dash PK, Pateriya A, Pattnaik B, Pradhan HK, et al. Development and evaluation of SYBR Green I-based one-step real-time RT-PCR assay for detection and quantitation of Japanese encephalitis virus. J Virol Methods. 2007;143:73–80.

    CAS  PubMed  Article  Google Scholar 

  86. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  88. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

We thank Dr. Jiaoer Zhang (Harbin Veterinary Research Institute) for assistance in the pathological diagnosis.

Funding

TQA was supported by grants from the National Natural Science Foundation of China (32072851, 31941019). MS was supported by the Shenzhen Science and Technology Program (KQTD20200820145822023), Guangdong Province “Pearl River Talent Plan” Innovation and Entrepreneurship Team Project (2019ZT08Y464), and the State Key Laboratory of Veterinary Biotechnology Research Fund (SKLVBF202110). ECH was supported by an Australian Research Council Australia Laureate Fellowship (FL170100022).

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Authors and Affiliations

Authors

Contributions

ECH, TQA, and MS designed the study. XYH, XXT, XH, XYC, YHX, QLJ, PZ, LQL, WLS, LGC, YS, YBY, JXC, GHZ, JLL, ECH, and XHC collected the samples and performed the experiments. XYH, WCW, HX, and MS performed the data analysis. XYH, WCW, and MS wrote the manuscript. ECH and ATQ edited the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Tongqing An or Mang Shi.

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Ethics approval and consent to participate

The sampling and experimental procedures for this study were reviewed by the ethics committee of the Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences (Approval Number: SY-2017-SW-018).

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1.

Table showing sample collection, pathogen abundance, and clinical signs.

Additional file 2.

RT-qPCR result showing the presence of JEV in sample NM84. Top panel: amplification plot and standard curve based on absolute standards as well as in sample NM84 (green curve and blue square). Bottom panel: Melt curve plots for the sample as well as a water control.

Additional file 3.

Phylogenetic relationships of the remaining viruses identified in this study. Sequences identified from the diseased group are marked with a red solid circle, whereas those from healthy controls are marked with a blue solid circle. For clarity, sequence names are not shown on the tree. The corresponding taxonomy/lineage and geographic information are provided on the right of the tree.

Additional file 4.

Distribution of disease symptoms associated with each pathogen. For each pathogen a bar graph shows the number of cases (x axis) associated with each symptoms (y axis).

Additional file 5.

Primer and probe sequence information.

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Huang, X., Wu, W., Tian, X. et al. A total infectome approach to understand the etiology of infectious disease in pigs. Microbiome 10, 73 (2022). https://doi.org/10.1186/s40168-022-01265-4

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Keywords

  • Infectome
  • Meta-transcriptomics
  • Animal disease
  • Pathogens