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Metagenomic screening of microbiomes identifies pathogen-enriched environments

  • Xiaofang LiEmail author
Open Access
Research

Abstract

Background

Human pathogens are widespread in the environment, and examination of pathogen-enriched environments in a rapid and high-throughput fashion is important for development of pathogen-risk precautionary measures. In this study, a Local BLASTP procedure for metagenomic screening of pathogens in the environment was developed using a toxin-centered database. A total of 69 microbiomes derived from ocean water, freshwater, soils, feces, and wastewater were screened using the Local BLASTP procedure. Bioinformatic analysis and Canonical Correspondence Analysis were conducted to examine whether the toxins included in the database were taxonomically associated.

Results

The specificity of the Local BLASTP method was tested with known and unknown toxin sequences. Bioinformatic analysis indicated that most toxins were phylum-specific but not genus-specific. Canonical Correspondence Analysis implied that almost all of the toxins were associated with the phyla of Proteobacteria, Nitrospirae and Firmicutes. Local BLASTP screening of the global microbiomes showed that pore-forming RTX toxin, ornithine carbamoyltransferase ArgK, and RNA interferase Rel were most prevalent globally in terms of relative abundance, while polluted water and feces samples were the most pathogen-enriched.

Conclusions

The Local BLASTP procedure was applied for rapid detection of toxins in environmental samples using a toxin-centered database built in this study. Screening of global microbiomes in this study provided a quantitative estimate of the most prevalent toxins and most pathogen-enriched environments. Feces-contaminated environments are of particular concern for pathogen risks.

Keywords

Metagenomics Microbiome Local BLASTP Toxins Pathogens 

Abbreviations

BLAST

basic local alignment search tool

PCR

polymerase chain reaction

Background

Rapid identification of pathogens in a particular environment is important for pathogen-risk management. Human pathogens are ubiquitous in the environment, and infections from particular environments have been reported worldwide. For example, soil-related infectious diseases are common [1, 2]. Legionella longbeachae infection has been reported in many cases, mainly due to potting mixes and composts [3]. Survival of enteric viruses and bacteria has also been detected in various water environments, including aquifers and lakes [4, 5, 6, 7].

Examination of pathogens from infected individuals with a particular clinical syndrome has been a major achievement of modern medical microbiology [8]. Nevertheless, we still know little about the magnitude of the abundance and diversity of known common pathogens in various environments, which is very important for the development of appropriate precautions for individuals who come in contact with certain environmental substrates. This can be realized through metagenomic detection of pathogenic factors in a time-efficient and high-throughput manner using next-generation sequencing methods [8].

Metagenomic detection of pathogens can be accomplished through different schemes. Li et al. [9] examined the level and diversity of bacterial pathogens in sewage treatment plants using a 16S rRNA amplicon-based metagenomic procedure. Quantitative PCR has also been applied for monitoring specific pathogens in wastewater [10]. More studies have applied the whole-genome-assembly scheme to detect one or multiple dominant pathogens, most of which were for viral detection in clinical samples [11, 12, 13, 14]. Although metagenomic-based whole-genome-assembly for bacterial pathogen detection can be conducted at the single species level [15], its computational requirements are high if it is in a high-throughput fashion. In 2014, Baldwin et al. [16] designed the PathoChip for screening pathogens in human tissues by targeting unique sequences of viral and prokaryotic genomes with multiple probes in a microarray. This approach can screen virtually all pathogen-enriched samples in a high-throughput manner.

Despite the aforementioned progress in metagenomic tools for pathogen detection, metagenomic screening for bacterial pathogens in environments such as soil, where microbial diversity is tremendous, is still challenging. This is mostly due to difficulty in assembling short reads generated by next-generation sequencing [8]. The whole-genome-assembly approach is efficient at identifying viromes, but not at dealing with bacterial pathogens from metagenomes especially when target pathogens are of low abundance. Amplicon-based approaches are able to detect bacterial pathogens in a high-throughput manner; however, it is well known that phenotypic diversity exists widely across and within microbial species of a genus because of divergent evolution [17, 18]. This also holds true for pathogenic factors [19]. Moreover, toxin factors, such as the Shiga toxin (stx) of Shigella, are primarily transferable through lateral gene transfer, which leads to the continuous evolution of pathogen species [20]. Therefore, it is necessary to examine the pathogen diversity in environmental metagenomes using essential virulence genes as biomarkers.

In this study, a toxin-centered virulence factors database was built, and the well-developed Local BLASTP method was applied to detect virulence factors in various environments. This procedure is metagenome-based and can be conducted in a high-throughput fashion, which greatly simplifies development of precautions for pathogen-enriched environments.

Methods and materials

Environments and their metagenomes

Sixty-nine metagenomes were selected and downloaded from the MG-RAST server (Table 1). These metagenomes were derived from ocean water, freshwater, wastewater, natural soil, deserts, and feces, representing the major environmental media found worldwide. Sequencing methods of the metagenomes include the illumina, Ion Torrent and 454 platforms, and predicted proteins in the metagenomes ranged from 33,743 (fresh water, ID mgm4720261) to 11,587,259 (grassland soil, ID mgm 4623645). The gene-calling results from the MG-RAST server were used for toxin factor screening in this study. The taxonomic composition at the genus level was also retrieved from the MG-RAST server for 27 representative metagenomes.
Table 1

General information regarding the metagenomes retrieved from the MG-RAST server

MG-RAST ID

Abbreviation

bp count

Sequence count

Protein predicted

Material

Location

Country

Coordinates

Sequencing method

mgm4440281

MW281

35,439,683

334,386

227,038

Mine drainage

Soudan Mine

USA

47.819, − 92.243

454

mgm4443754

WW754

24,210,189

54,344

58,182

Waste water

Singapore

Singapore

1.332, 103.754

Unknown

mgm4443755

WW755

30,035,399

57,827

62,600

Waste water

Singapore

Singapore

1.332, 103.755

Unknown

mgm4443756

WW756

29,292,896

61,672

65,312

Waste water

Singapore

Singapore

1.332, 103.756

Unknown

mgm4461675

FW675

156,809,137

389,864

258,015

Well water

Lashkardi village

Bangladesh

23.744, 90.606

454

mgm4507016

F016

163,648,718

227,551

250,347

Feces

Bologna

Italy

44.495, 11.343

Illumina

mgm4510939

FW939

76,571,419

97,768

106,497

Surface water

Minnesota

USA

43.000, − 91.000

Illumina

mgm4510941

FW941

49,162,469

56,117

68,954

Surface water

Minnesota

USA

47.000, − 95.000

Illumina

mgm4510942

FW942

26,796,913

42,585

49,581

Surface water

Minnesota

USA

45.000, − 94.000

Illumina

mgm4510943

FW943

52,372,093

74,178

86,925

Surface water

Minnesota

USA

45.000, − 94.000

Illumina

mgm4510944

FW944

53,884,243

72,536

84,028

Surface water

Minnesota

USA

44.000, − 93.000

Illumina

mgm4510945

FW945

54,142,157

71,478

83,751

Surface water

Minnesota

USA

44.000, − 93.000

Illumina

mgm4510946

FW946

49,216,363

74,745

83,253

Surface water

Minnesota

USA

43.000, − 93.000

Illumina

mgm4514299

DS299

322,114,449

242,0832

1,323,378

Saline Desert Soil

Gujarat of India

India

23.7925, 71.008

Ion torrent

mgm4533707

OW707

134,833,790

668,257

508,217

Brackish Water

Columbia River coastal margin, OR & WA

USA

46.265, − 123.999

Illumina

mgm4543019

DS019

282,578,916

2,016,127

842,475

Saline Desert Soil

Gujarat of India

India

23.908, 70.538

Ion torrent

mgm4546371

WW371

84,424,005

907,785

803,682

Wastewater

Universiti Teknologi Malaysia

Malaysia

2.558, 104.642

Illumina

mgm4556493

AS493

162,926,938

1,613,138

1,332,131

Activated sludge

Unkown

China

31.550, 120.315

Illumina

mgm4556497

AS497

162,926,938

1,613,138

1,278,907

Activated sludge

Unkown

China

31.551, 120.316

Illumina

mgm4556505

AS505

257,249,323

2,547,023

1,737,663

Activated sludge

Unkown

China

31.552, 120.316

Illumina

mgm4556509

AS509

41,926,615

415,115

330,475

Activated sludge

Unkown

China

31.552, 120.317

Illumina

mgm4560423

F423

22,734,940

73,479

76,569

Feces

Lake Eyasi, Tanzania

Tanzania

− 3.635, 35.083

Illumina

mgm4568577

MW577

10,065,266

50,137

34,287

Mine water

Guangdong

China

24.503, 113.710

454

mgm4568580

MW580

12,911,442

62,018

36,461

Mine water

Guangdong

China

22.940, 112.050

454

mgm4589537

FW537

337,068,782

2,099,471

1,842,975

Surface water

West Virginia

USA

38.094, − 81.959

Illumina

mgm4620487

WW487

147,523,219

696,132

640,283

Wastewater

Guelph ON Canada

Canada

43.544, − 80.248

Illumina

mgm4620488

WW488

115,131,556

578,337

537,267

Wastewater

Guelph ON Canada

Canada

43.545, − 80.248

Illumina

mgm4620491

BS491

52,759,415

244,855

238,630

Biosolides

Guelph ON Canada

Canada

43.545, − 80.248

Illumina

mgm4623645

S645

1,541,232,730

15,259,730

11,587,259

Grassland soil

MPG_Ranch

USA

46.682, − 114.027

Illumina

mgm4626292

S292

711,577,500

7,115,775

5,992,554

Mountain soil

Taishan, Guangdong, China

China

22.110, 112.770

Illumina

mgm4629146

D146

1,693,361,056

9,120,345

3,914,215

Dust

Valencia

Spain

39.466, − 0.366

Illumina

mgm4654022

S022

830,740,317

4,484,452

4,110,948

Mountain soil

northern Galilee Mountains

Israel

33.000, 35.233

Illumina

mgm4654023

S023

711,633,780

3,762,792

3,377,414

Soil

Terra Rossa

Israel

31.700, 35.050

Illumina

mgm4654025

S025

698,042,789

3,808,872

3,379,500

Soil

Terra Rossa

Israel

31.700, 35.050

Illumina

mgm4654028

S028

572,066,482

3,129,422

2,798,806

Soil

Rendzina

Israel

31.033, 34.9

Illumina

mgm4679248

S248

603,919,746

3,365,512

1,361,948

Soil

Seoul

South Korea

37.460, 126.948

Illumina

mgm4679254

S254

689,019,062

3,688,750

1,966,121

Soil

Seoul

South Korea

37.459, 126.948

Illumina

mgm4695622

PFW622

114,430,648

111,889

148,833

Polluted fresh water

Nanjing, Jiangsu

China

32.600, 118.160

Illumina

mgm4695626

PFW626

86,732,360

78,621

111,489

Polluted fresh water

Nanjing, Jiangsu

China

32.400, 118.140

Illumina

mgm4697397

OS397

143,214,978

397,067

299,940

Organic Soil

Beijing

China

32.054, 118.763

Illumina

mgm4713197

OW197

60,417,678

272,918

140,195

Ocean water

Moorea

Pacific Ocean

17.538, − 149.829

Illumina

mgm4713202

OW202

89,726,117

442,552

254,139

Ocean water

Moorea

Pacific Ocean

17.538, − 149.829

Illumina

mgm4713205

OW205

106,474,596

476,363

235,777

Ocean water

Moorea

Pacific Ocean

17.538, − 149.829

Illumina

mgm4718752

F752

329,518,322

1,312,822

950,489

Feces

Upstate NY

USA

42.668, − 76.528

Illumina

mgm4719940

OW940

360,335,259

1,425,556

1,023,445

Ocean water

Irish Sea

Atlantic Ocean

53.225, − 4.159

Illumina

mgm4720261

FW261

35,487,527

6,896

33,743

Fresh Water

Galway

Ireland

53.276, − 9.060

Illumina

mgm4740560

MT560

739,577,348

7,322,548

6,102,709

Mine tailing

Kamloops

Canada

Unkown

Illumina

mgm4763187

F187

1,605,883,158

10,011,241

3,022,645

Feces

Palmerston North

New Zealand

− 40.355, 175.612

Illumina

mgm4763293

F293

1,621,471,323

9,844,890

3,391,304

Feces

Palmerston North

New Zealand

− 40.355, 175.612

Illumina

mgm4763371

F371

1,455,357,314

9,241,610

2,388,304

Feces

Palmerston North

New Zealand

− 40.355, 175.612

Illumina

mgm4770614

S614

55,225,990

132,807

144,038

Forest soil

Leningrad

Russia

59.549, 31.399

Illumina

mgm4779571

DS571

226,100,954

1,707,504

1,288,831

Desert soil

Kutch Desert

India

23.941, 70.188

Ion Torrent

mgm4779573

OW573

126,222,705

403,606

341,516

Saline water

Lake Tyrrel

Australia

− 35.32, 142.8

454

mgm4779575

OW575

46,134,631

135,959

130,566

Saline water

Albufera

Spain

39.332, − 0.352

454

mgm4779577

OW577

26,544,332

80,951

44,824

Saline water

Lake Tyrrel

Australia

− 35.320, 142.800

454

mgm4779580

OW580

10,257,469

80,645

60,571

Saline water

British_Columbia

Canada

49.730, − 119.874

Ion Torrent

mgm4779582

OW582

236,630,443

691,427

455,629

Saline water

Santa Pola

Spain

38.200, − 0.600

454

mgm4779585

SE585

69,798,594

852,769

442,408

Sediment

Yilgarn_Craton

Australia

− 33.426, 121.689

Illumina

mgm4779589

OW589

10,713,143

81,307

61,739

Saline water

British Columbia

Canada

49.730, − 119.874

Ion torrent

mgm4784118

SE118

294,673,007

263,221

400,006

Sediment

St. Francis Bay

South Africa

− 34.190, 24.704

Ion Torrent

mgm4784267

SE267

337,590,087

280,494

451,716

Sediment

Cape Recife

South Africa

− 34.045, 25.569

Ion Torrent

mgm4795328

FW328

1,263,293,418

5,301,996

2,970,195

Surface water

San Antonio

USA

29.424, − 98.493

Illumina

mgm4819059

S059

658,065,701

4,358,051

2,432,031

Forest soil

Lahti

Finland

60.971, 25.704

Illumina

mgm4819062

S062

1,197,697,874

7,931,774

6,478,848

Forest soil

Baltimore

USA

39.488, − 76.689

Illumina

mgm4819067

S067

771,429,857

5,108,807

3,703,810

Grassland soil

Potchefstroom

South Africa

− 26.701, 27.101

Illumina

mgm4819073

S073

730,894,511

4,840,361

3,946,449

Forest soil

Baltimore

USA

39.326, − 76.622

Illumina

N/A

SRS

532,850,584

1,632,914

1,408,943

Red Soil

Mt Isa

Australia

20.440, 139.300

Illumina

N/A

SRSP

433,386,397

1,338,665

1,081,822

Red soil polluted

Mt Isa

Australia

20.440, 139.300

Illumina

N/A

SSLS

507,124,889

1,552,234

1,413,889

Shrub land soil

Mt Isa

Australia

20.440, 139.300

Illumina

Toxin factor database

A toxin-centered database was established for bacterial pathogen detection in metagenomes in this study. Candidate toxin factors for pathogenic screening of environmental metagenomes were gathered based on well-studied pathogens summarized in the Virulence Factor Database [21], a soil borne pathogen report by Jeffery and van der Putten [2], and a manure pathogen report by the United States Water Environment Federation [22]. Sequences of the toxin factors were then retrieved by searching the UniProt database using the toxin plus pathogen names as an entry [23], while typical homologs at a cut-off E value of 10−6 were gathered from GenBank based on BLAST results. A protein database was then built for Local BLSATP study (Additional file 1). Considering that virulence process involves several essential factors including toxins, various pathogen-derived secretion proteins were also included in the database, and it was tested that whether secretion proteins were as specific as toxin proteins for pathogen detection. The disease relevance of all virulence factors was screened using the WikiGenes system [24] and relevant publications (Table 2).
Table 2

Typical virulence factors investigated in this study and their disease–relevance

Toxin factor

ID in the databse

Typical pathogens and (disease)

Role of the toxin

Reference

Aerolysin

aerA

Aeromonas spp.

Cytolytic pore-forming

[25]

Alveolysin

alo

Bacillus anthracis

Pore-forming

[26]

Dermonecrotic toxin

dnt

Bordetella pertussis

Stimulating the assembly of actin stress fibers and focal adhesions

[27, 28]

Pertussis toxin subunit 1

ptxA

Bo. pertussis

Causing disruption of host cellular regulation

[28]

Type IV secretion system protein Ptl

ptlCH

Bo. pertussis

Secretion of pertussis toxin

[28]

Chlamydia protein associating with death domains

CADD

Chlamydia trachomatis (trachoma, urethritis, etc.)

Inducing cell apoptosis

[29]

Perfringolysin O

pfo

Clostridium perfringens (food poisoning)

Pore-forming

[30]

Glucosyltransferase toxin B

toxB

Cl. sordellii

Cl. difficile (diarrhea)

Cytopathic effects

[31]

Zeta toxin family protein

ZETA

Coxiella sp. DG_40

Inhibiting cell wall biosynthesis

[32]

Shiga toxin 1

stx1

Escherichia coli (diarrhea)

Shigella dysenteriae (Shigellosis)

Haemolytic uraemic syndrome

[20]

Toxin CdiA

cdiA

E. coli

Yersinia pestis (plaque)

Decreasing aerobic respiration and ATP levels

[33]

Shiga-like toxin 2

stx2

Enterobacteria phage 933 W

E. coli

Haemolytic uraemic syndrome

[34]

Repeats-in toxin

rtxA

Legionella pneumophila (Legionnaries’ disease)

Aeromonas dhakensis (gastroenteritis, septicemia)

Adherence and pore forming

[35, 36]

Cholera toxin secretion protein EpsF

epsF

Le. pneumophila

Toxin secretion

[37]

Toxin secretion ATP binding protein

LwT1SS

Le. waltersii

Toxin secretion

[38]

1-phosphatidylinositol phosphodiesterase

PLC

Listeria monocytogenes (listeriosis)

Lysis of the phagolysosomal membrane

[39]

Listeriolysin O

hly

Li. monocytogenes (listeriosis)

Pore forming, hemolysin

[40, 41]

Outer membrane channel protein CpnT

cpnT

Mycobacterium tuberculosis

Nutrient uptake

[42]

RNA interferase

mazF/pemK/ndoA/relE/relK/relG/yoeB/higB/mvpA

Proteus vulgaris (wound infections)

My. tuberculosis (tuberculosis)

E. coli

Cleavage of cellular mRNAs, inhibiting growth

[43, 44, 45, 46, 47, 48]

Hemolytic phospholipase C

plcH

Pseudomonas Aeruginosa

Cl. perfringens (food poisoning)

Membrane-damaging

[49]

ADP-ribosyltransferase toxin

exoS

Ps. aeruginosa

Inhibition of phagocytosis

[50]

Ornithine carbamoyltransferase

argK

Ps. savastanoi

Promoting survival and pathogenicity

[51]

Exoenzyme U

exoU

Ps. aeruginosa

Membrane-lytic and cytotoxic

[52]

Exotoxin A

ETA

Ps. aeruginosa (eye and wound infections)

ADP-ribosylating eukaryotic elongation factor 2

[53]

Mono(ADP-ribosyl)transferase

spvB

Salmonella dublin (gastroenteritis)

ADP-ribosylating, destabilizing cytoskeleton

[54]

Adenylate cyclase

cyaA

Sa. choleraesuis (typhoid fever)

Bo. pertussis (whooping cough)

Ba. anthracis (anthrax)

Pore-forming with cAMP-elevating activity

[55]

Endonuclease VapC

vapC

Shigella flexneri (diarrhea)

Sa. Dublin

My. Tuberculosis

Coxiella burnetii (Q fever)

tRNase activities

[45]

Leucotoxin

luk

Staphylococcus aureus (sinusitis, skin abscess)

Lysis of leukocytes

[56]

Exfoliative toxin

ET

St. aureus

Proteolytic activity

[57]

Local BLASTP

The Local BLASTP was applied following the procedure used in our previous study [58, 59]. Basically, the gene-calling results of each metagenome were searched against the toxin factor database using BLASTP. The cut-off expectation E value was set as 10−6. The results of the Local BLASTP were then copied to an Excel worksheet, after which they were subjected to duplicate removal, quality control, and subtotaled according to database ID. Duplicate removal was based on the hypothesis that each sequence contains one copy of a specific toxin factor, since the gene-calling results were used. For quality control of the BLAST results, a cut-off value of 40% for identity and 20 aa [1/3 of the length of the shortest toxin factors (e.g., the Heat-Stable Enterotoxin C)] for query alignment length were used to filter the records. The toxins abundance matrix was formed for subsequent analyses.

Specificity tests of the Local BLASTP method

Sequences from the toxin database established in this study, as “known sequences” to the database, were selected randomly and searched against the database using the BLASTP procedure. The genome of Clostridium perfringens ATCC 13124 (NC_008261), as “unknown” sequences to the database, was subject to the Local BLASTX procedure as well. Homologous proteins were searched exhaustively in the GenBank database using BLASTP, with the representative toxin factors in the toxins database as a query. Sequences were retrieved and aligned using ClustalW [60], and Maximum-likelihood phylogeny was conducted with MEGA 7 [61].

Data analysis

The toxin frequency in each metagenome was normalized to a total gene frequency of 10,000,000 to eliminate the effects of gene pool size. Toxin abundance in the 69 metagenomes was visualized using Circos [62]. The genus abundance of 27 selected metagenomes representing the main environment types was calculated and sorted by genus name, followed by manual construction of a genus abundance matrix for subsequent biodiversity-toxin abundance Canonical Correspondence Analysis using R [63] with the package ‘vegan’ [64].

Results and discussion

In this study, a toxin-centered database was built for bacterial pathogen screening in various microbiomes through a Local BLASTP procedure. The specificity of the procedure was tested, the relative abundance of toxins in the microbiomes was examined, and the toxin-taxonomic abundance correspondence analysis was performed.

Like the previously established Local BLASTN method for antibiotic and metal resistance genes screening [58, 59, 65], the Local BLASTP method using the toxin-centered pathogen database in this study was successful at accurately identifying toxin proteins from the database. For screening of the Clostridium perfringens ATCC 13124 genome, the methods successfully detected the pore-forming genes and multiple copies of the glucosyltransferase (toxB-like) and ADP-ribosyltransferase (spvB-like) genes, based on the raw data. These results are consistent with the virulence genetic features of Clostridium sp. [21], which have not been well detailed in the GenBank annotation record. Such a cross-validation positively indicated that the Local BLASTP procedure established here is useful in predicting toxin genes in unknown genomes. Yet for a semi-quantitative method to estimate toxin factors in metagenomes, a false positive analysis is required to examine to what level mismatch is included in the Local BLASTP results. Actually, the cut-off values of identity greatly impact the homolog virulence factor abundance returned. At cut-off values of 40% for identity and 20 aa for alignment length, only four records for Clostridium perfringens ATCC 13124 genome query were returned after duplication removal, one for 1-phosphatidylinositol phosphodiesterase, one for pore-forming alveolysin, one for Ornithine carbamoyltransferase and one for RNA interferase NdoA. At a cut-off identity value of 35%, one more record (Toxin secretion ATP binding protein) was returned. This means that the Local BLASTP procedure was able to detect the virulence factors in unknown genomic dataset at least semi-quantitatively, with proper cut-off values for data quality control. The accuracy of the BLASTP procedure in virulence factor detection was further tested using the genomes of Bacillus thuringiensis serovar konkukian str. 97-27 (AE017355.1) and Helicobacter pylori 26695 (AE000511.1).

As mentioned above, functional genes including toxin factors may partly evolve through lateral gene transfer, which makes their taxonomic affiliation difficult. It is thus interesting to explore how specific toxin factors are associated with the taxonomic units of pathogens. Here, I explored this issue by investigating the taxonomic distribution of homologs of toxins retrieved from the GenBank database. Generally, at a lower expectation value, most toxins were associated with a specific group of pathogens. For example, at the default cut-off E value, 241 out of 242 returned records of Mycobacterium tuberculosis RelE homologs fell within the phylum Actinobacteria. Moreover, 89% of these homologs were from the genus Mycobacterium, while 99.7% of Yersinia pestis CdiA homologs and 92.7% of Bordetella pertussis cya homologs belonged to Proteobacteria, and homologs of Aeromonas dhakensis repeats-in toxin (RtxA) were mostly associated with the class Gammaproteobacteria (206 out of 242). However, no obvious genus-toxin association was identified. It is worth noting that these results largely depended on the availability of toxin sequences in each taxonomic unit. The lack of a genus-toxin association basically denied the possibility of detecting a specific pathogen using a specific toxin as a single signature.

It is still not clear whether virulence secretion proteins are specific for pathogen detection as signatures, though they are essential for virulence process [20]. For example, the contact-dependent toxin delivery protein CdiA was found to be widespread in bacteria [33]. The relative abundance of secretion proteins in the 69 microbiomes was determined as well as that of the toxins which are essential to virulence processes. The results of the present study showed that the abundance of secretion proteins selected in the database was strongly correlated with the toxin abundance (R2 = 0.74, P = 0.0068, Fig. 1). The most abundant secretion proteins included L. waltersii toxin secretion protein (LWT1SS), L. pneumophila toxin secretion protein ApxIB, and Aeromonas hydrophila RTX transporter (RtxB) (data not shown). Further exploration indicated that although A. hydrophila RtxB homologs from GenBank were found in all Proteobacteria classes, most of the RtxB-harboring species have been reported to be pathogens, including Vibrio spp. [64, 66], Pseudomonas spp., Neisseria meningitides [67], Ralstonia spp. [68], and Yersinia spp. [21]. This may imply the pathogen-specific nature of secretion proteins included in the database, and that toxin secretion proteins can be used as signatures for pathogen detection as well.
Fig. 1

Correlation between relative abundance of toxins and secretion proteins in the global microbiomes (N = 69)

Toxin-phyla CCA results showed that all phyla can be clearly separated into two groups, and that almost all toxins were associated with Proteobacteria, Nitrospirae, and Firmicutes (Fig. 2). Considering the phylum-specificity of the toxins stated above, these results can be biased because of the taxonomic affiliation of toxins included in the Local BLASTP database. The taxonomic distribution proportion of currently available genomes of identified pathogens was reflected in the toxin database, with Proteobacteria and Firmicutes accounting for the majority of the genomes. However, the CCA results may also indicate, at least in part, a proportional lack of pathogens in some phyla, such as Crenarchaeota, Euryarchaeota, Verrucomicrobia, and Bacteroidetes [69]. Archaea cannot easily absorb phage particles because of their extracellular structures, which differ from bacteria [70]. A recent study by Li et al. [9] also found that the five most abundant bacterial pathogens were from either Proteobacteria or Firmicutes in wastewater microbiomes. Taken together, these findings could indicate that Proteobacteria or Firmicutes were evolutionarily enriched with pathogens when they dominated most environmental microbiomes on the planet [71, 72].
Fig. 2

Canonical correspondence analysis of the associations between phyla and toxins from typical environments

Interestingly, there was a strong association between the phylum Nitrospirae and toxins of RNase inteferases (MvpA and MapC) and Listeria monocytogenes1-phosphatidylinositol phosphodiesterase PLC. Further searches against the UniProt database [73] revealed no homologous records of MvpA and PLC from Nitrospirae, and only 109 out of 15,574 bacterial records for VapC were from Nitrospirae. These findings imply that there may be many more Nitrospirae pathogens harboring MvpA and PLC that have yet to be discovered.

The screening of toxins in the 69 global microbiomes revealed the most prevalent toxins and pathogen-enriched environments. Specifically, the results showed that pore-forming RTX toxin and ornithine carbamoyltransferase ArgK were most prevalent globally in terms of both occurrence and relative abundance (Fig. 3). RTX toxins comprise a large family of pore-forming exotoxins. Known homologs in the GenBank database of Aeromonas dhakensis RtxA were mainly in the genera of Aeromonas, Pseudomonas (e.g., CP015992), Vibrio (e.g., CP002556), and Legionella (e.g., CP015953). These genera are well known to be associated with gastroenteritis, eye and wound infections, cholera and legionellosis, and RTX toxins are a key part of the virulence systems of each of these conditions [74, 75, 76, 77]. The argK gene is a part of the Pht cluster, which contains genes for the synthesis of phaseolotoxin in Ps. syringae pv. phaseolicola [78]. ArgK plays an essential role in the survival and pathogenicity of Ps. syringae. Known ArgK proteins mainly come from Pseudomonas, Escherichia, and Mycobacterium, which are widespread and persistent in the environment [79]. In addition, Cya is worth noting as an essential unit of Bacillus anthracis virulence that causes anthrax and may lead to mammalian death [80]. Known homologs in the GenBank database of Bacillus anthracis Cya were mainly from Bacillus spp., Bordetella spp., Pseudomonas aeruginosa, Yersinia pseudotuberculosis, and Vibrio spp. Their presence in the environment should be carefully examined and precautions should be taken to prevent infection by these organisms since many of them are associated with very common diseases such as whooping cough.
Fig. 3

Circular visualization of the toxin abundance in the microbiomes selected from locations worldwide. The designated environment abbreviation can be found in Table 1

The main purpose of the Local BLASTP method established here was to screen pathogen-enriched environments to enable development of precautionary measures. Our results clearly indicated that contaminated freshwater, feces, and harbor sediment microbiomes were rich in pathogens (Fig. 4). Although there was no detailed background information regarding these environments in this study, the results presented herein may provide important implications for pathogen-related risk control. Surprisingly, two lake water microbiomes from Nanjing, China contained the highest toxin factors among the 69 samples. Further investigation of the location and contamination status supported the sewage-nature of the lake water. In China, most polluted lakes receive sewage that includes feces materials [81]. According to an official survey conducted in 2015, Nanjing has 28 lakes with a total area of 14 km2, among which 96.4% are classified as polluted (Class V of the national standard). Studies have documented that pathogens tend to be enriched in polluted waters [13]. It is not surprising to find that feces samples had very high abundance of toxins. Epidemical statistics have indicated that feces are the most important pathway for diarrheal diseases, which is a leading cause of childhood death globally [82]. Meanwhile, dry soil environments like desert soil and desert tailings were found to contain relatively less toxin factors. It is still unclear to what extent the environments stressed by long-lasting drought or metal pollution suppress the colonization and development of pathogens [83]. In all, the association between environmental factors and pathogen abundance merits a systematic exploration in the future.
Fig. 4

A Boxplot showing the relative abundance of toxins detected from the metagenomes in this study. Drysoil includes the desert soil and desert mine tailings

Conclusions

A Local BLASTP procedure was established for rapid detection of toxins in environmental samples. Screening of global microbiomes in this study provided a quantitative estimate of the most prevalent toxins and most pathogen-enriched environments.

Notes

Acknowledgements

I thank Dr. Philip L. Bond and The University of Queensland for providing training in bioinformatics. I would like to thank LetPub (http://www.letpub.com) for providing linguistic assistance during the preparation of this manuscript. I also thank the founders of the existing pathogen-relevant database, particularly the Virulence Factor Database, which provided valuable reference for the build-up of the toxin database in this study.

Authors’ contributions

XL initiated the study, analyzed the data, and wrote the manuscript. The author read and approved the final manuscript.

Funding

This work was financially supported by the National Key Research and Development Program of China (2018YFD0800306), the National Natural Science Foundation of China (41877414), and Hebei Science Fund for Distinguished Young Scholars (D2018503005).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12302_2019_217_MOESM1_ESM.txt (62 kb)
Additional file 1. A toxin factor database for metagenomic detection of environmental pathogens through Local BLASTP.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Centre for Agricultural Resources Research, Institute of Genetics and Developmental BiologyChinese Academy of SciencesShijiazhuangChina

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