Use of Big data – Metagenomic Analysis to Study Environmental Dissemination of ARGs

Tong Zhang

1 Environmental Biotechnology Laboratory Department of Civil Engineering

The University of Hong Kong (HKU)

2 School of Public Health, HKU

Regional Symposium on AMR 2018, Hong Kong, Nov. 13, 2018

3 International Center for Antibiotic Resistance in the Environment (iCARE)

Southern University of Science and Technology, Shenzhen, China

2

- The Review on Antimicrobial Resistance (2016).

3

Antibiotics and Antibiotics Resistance Genes in Environment (Frontiers Report 2017, UNEP)

4

AMR from Environmental Pollution

• Yang, Y., Li, B., Ju, F. and Zhang, T. (2013). Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach. Environmental Science & Technology, 47(18), 10197-10205.

• Zhang, T. (2016). Antibiotics and resistance genes in wastewater treatment plants. AMR Control, 9 July 2016. 5

A typical STP (sewage treatment plant) in Hong Kong

Activated sludge: an old process with >100 years history.

AS floc: aggregate of billions of microbial cells.

http://cgi.tu-harburg.de/~awwweb/wbt/emwater/lessons/lesson_c1/lm_pg_1425.html

• Created by environmental engineers, which not exist naturally. • High diversity : thousands species. • High biomass density : 2 ~ 50 g/L ( ~ 1013 cells/L, assuming dry

weight of a bacteria cell is ~ 2 × 10-13 g). • Bacteria are close to each other in the flocs of activated sludge,

granule sludge or biofilm, making HGT easier.

• SRT (sludge retention time, average generation time) of activated sludge: 6~12 days, 30~60 generations in a year

• Bacteria from fecal waste of thousands (103-106) peoples.

• Selective pressure: almost all the antibiotics, heavy metal, etc. • Concentrated antibiotics in the micro-environment formed by

EPS (extracellular polymeric substances)

• Bacteria discharged in effluent or sludge.

Microbiome in WWTPs as Hot Spots of ARGs

7

• Isolation of resistant bacteria based on their phenotypes using selective media with specific antibiotic(s).

• PCR/sequencing to investigate genotypes of ARGs.

• qPCR to quantify ARGs in relative/absolute ways. • High-throughput qPCR and microarray for wide spectrum of

ARGs if those primers/probes are available.

• …....................

• Metagenomic tools for a complete list of known and unknown/novel ARGs (via functional metagenomics)

Methods Used to Study ARGs in the Environment

8

http://www.wikiwand.com/en/Carlson_curve

Moore’s Law

Carlson Curve

9

Bioinformatics : translation (from the different combinations of A, T, G and C to some biological terms, such as names of bacteria species and names of genes/enzymes) of big data, based on databases (like “dictionaries”). Bioinformatics : another kind of the “microscope” to study microorganisms in wastewater reactors. It tells us the names and functions of different microbial populations.

Bioinformatics A “New Frontier” in environmental microbiology

10

Abundance of ARGs / ppm

IN_1 526 595

IN_2 664 EF_1 84.0

82.6 EF_2 81.2 AS_1 28.8

29.9 AS_2 31.0

ADS_1 58.2 47.4

ADS_2 36.6

ARGs Profile in a full-scale WWTP at Hong Kong

Yang Y, Li B, Zou SC, Fang HHP, Zhang T*. 2014. Fate of antibiotic resistance genes in sewage treatment plant revealed by metagenomic approach. Water Research. 62, 97-106. 11

ppm (part per million, one ARGs-like sequence per million sequences)

DW

.Source.Feb.12

DW

.Source.Jul.11

DW

.Source.Jul.12a

DW

.Source.Jul.12b

DW

.Tap.Feb.12D

W.Tap.Jul.11

DW

.Tap.Jul.12

ST.S

TP.Influent.S

umm

erS

T.STP

.Influent.Winter

ST.S

TP.E

ffluent.Sum

mer

ST.S

TP.E

ffluent.Winter

ST.S

TP.A

S.Jul.07

ST.S

TP.A

S.Jan.08

ST.S

TP.A

S.Jul.08

ST.S

TP.A

S.Jan.09

ST.S

TP.A

S.Jul.09

ST.S

TP.A

S.Jan.10

ST.S

TP.A

S.Jul.10

ST.S

TP.A

S.Jan.11

ST.S

TP.Foam

ingAS

.Mar.12

ST.S

TP.A

S.M

ar.12S

L.STP

.AS

.Mar.12a

SL.S

TP.A

S.M

ar.12bS

L.STP

.AS

.Mar.12

SL.S

TP.B

F.Mar.12

ST.S

TP.A

DS

.Sep.11

ST.S

TP.A

DS

.Mar.12

SW

H.S

TP.A

DS

.Sep.11

SW

H.S

TP.A

DS

.Mar.12a

SW

H.S

TP.A

DS

.Mar.12b

Hum

an_gut_1H

uman_gut_2

LWT.Fishpond.S

ediment

ST.Fishpond.S

ediment

YTT.Fishpond.S

ediment

Soil.1

Soil.2

Soil.3

Faeces.pig.1month.a

Faeces.pig.1month.b

Faeces.pig.1month.c

Faeces.pig.8month.a

Faeces.pig.8month.b

Faeces.pig.8month.c

Pigfarm

.STP

.InfluentP

igfarm.S

TP.E

ffluentFaeces.C

hicken.20d.aFaeces.C

hicken.20d.bFaeces.C

hicken.80d.aFaeces.C

hicken.80d.b

AcridineAminoglycosideBacitracinBeta-lactamBicyclomycinBleomycinChloramphenicolFosfomycinFosmidomycinMLSMultidrugOthersPolymyxinQuinoloneSulfonamideTetracyclineTrimethoprimVancomycin

River water

Drinking water

STP influentsSTP effluents

STP AS & BF STP ADS

Human gutSediments

Soils Faeces & wastewater from livestock farm

ND

10-6

10-4

10-2

100

(A)

Abundance of ARGs in Different Environments

• The abundant ARGs were usually associated with the extensively used antibiotics. • The abundance of ARGs increased with the influence of anthropogenic activities.

1E-3

0.01

0.1

1

Group IV

Group III

Faec

es &

Wastew

ater

from

lives

tock f

arm

Soils

Sedim

ents

Human

gut

STP A

DS

STP A

S & BF

STP e

ffluen

ts

STP i

nflue

nts

Drink

ing w

ater

Copy

of A

RG/C

opy o

f 16S

-rRNA

gene

River

water

4

Group I

Group II

Mean75%50%25%

Min

Max

(B)

12 Li B, Yang Y, Ma LP, Ju F, Guo F, Tiedje JM, Zhang T*. 2015. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME. 9(11), 2490-502.

Xinjiang

Tibet

Qinghai

Inner Mongolia

Gansu

Sichuan

Yunnan Guangxi

Guizhou

Guangdong

Heilongjiang

Jilin

Liaoning

Hebei

Shandong

Shaanxi

Ningxia Shanxi

Hunan

Jiangxi

Hong Kong

Macau

Hainan

TaiwanFujian

ZhejiangHubei

Henan

Jiangsu

AnhuiShanghai

BeijingTianjin

S01

S02S03

S04S05

S06

S07S08

S09

S10S11

S12

S13S14

S15

S16

S17S18

S19

S20S21

S22

S23

S25

S24Johannesburg, South Africa

CA, USA

Singapore

Resistance level I (<0.1)Resistance level II (0.1~0.2)Resistance level III (>0.2)

Abundance of ARGs (copy of ARG per cell, capc)

10 50 100Diversity

Diameter

SXXSXXSXX

Pie Chart (top 3 ARG types)

MLS: Macrolide-lincosamide-streptogramin. •Color of sample ID showed the resistance level of ARGs (I, II and III). •Pie chart presented the profiles of ARG abundance (top 3 ARG types). •Diameter of pie chart indicated the ARG diversity (number of ARG subtypes).

Number of

samples

Percentage of

samples Resistance level I (<0.1) 14 56% Resistance level II (0.1~0.2) 9 36% Resistance level II (>0.2) 2 4%

16 ARG types

181 ARG subtypes

2.8E-2 ~ 4.2E-1 capc

2~35 Cells

Ma LP, Li B, Jiang XT, Wang YL, Xia Y, Li, AD Zhang T*. 2017. Catalogue of Antibiotic Resistome and Host-tracking in Drinking Water Deciphered by a Large Scale Survey. Microbiome, 5:154.

Antibiotic Resistome in Drinking Water of China

SARG v1.0 & SARG v2.0

SARG Database and Analysis Pipeline

14

Database - SARG v1.0

Yang Y, Jiang XT, Chai BL, Ma LP, Li B, Cole J, Tiedje MJ*, Zhang T*. 2016. ARGs-OAP: Online Analysis Pipeline for Antibiotic Resistance Genes Detection from Meta-genomic Data Using an Integrated Structured ARG-database. Bioinformatics. 32(15):2346-51

CARD (15-04-2014,

2513 sequences)

ARDB (version 1.1,

7828 sequences)

586

Integrated database of ARGs

Removing non-ARG sequences

Removing redundant sequences

Removing SNP sequences

The Structured Antibiotic Resistance Genes Database

(SARG)

Type 1 Type 2 Type 3 ……

Subtype 1 Subtype 2 Subtype 3 ……

Key word search Literature review

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Database - SARG v2.0

Classify sequences by similarity search

Classify sequences by keywords search

Remove partial proteins

Type1 Type2 … ...

Subtype1 Subtype2 … ...

Type1 Type2 … ...

Subtype1 Subtype2 … ...

Retain those sequences with matched classification

Merge with SARG v1.0 database

Remove duplicates

Yin XL, Jiang XT, Chai BL, Ma LP, Yang Y, Cole J, Tiedje MJ*, Zhang T*. 2018. ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics. 34(13):2263-2270 16

Version Type Subtype

Sequences

SARG v1.0 24 1209 4049

SARG v2.0 24 1208 12307

Analysis pipeline - ARGs-OAP v2.0 You may select online or offline mode

Annotation Align metagenomic sequences against SARG database Annotate the sequences based on a cut-off (80% similarity and 75% hit length) strategy.

Classification and quantification ARGs are classified and quantified in both type and subtype levels.

Normalization Normalize ARGs abundance in both type and subtype levels to three units: ppm (part per million, one ARGs-like sequence per

million sequences) copies of ARGs/16S rRNA gene copies copies of ARGs/cell number

17

Online Metagenomic Analysis of ARGs (ARGs-OAP) Online metagenomic analysis became possible since the size of the metagenomic data set to be uploaded can be reduced significantly by UBALST pre-screening. Thus save the data uploading time by hundreds times. http://smile.hku.hk/SARGs

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https://nanoporetech.com

GridION X 5 MinION

PromethION SmidgION

• 3rd generation sequencing

• Fast sequencing (24h) with longer

reads

• Long reads (>70kb) enable

simultaneous host tracking

Oxford Nanopore (MinION)

19

Metagenomics sequencing using Oxford Nanopore (MinION)

20 Unpublished data (under submission)

Host tracking of ARGs in WWTPs

21 Unpublished data (under submission)

Individually, bacteria/ARGs will beat us one by one eventually. However, they can never beat our human being as a team.

Together we will stand divided we'll fall Come on now people let's get on the ball And work together, come on, come on Let's work together, now, now people Say now together we will stand, every boy, girl, woman, and man. by Wilbert Harrison （and by Canned Heart and Bryan Ferry）

https://www.youtube.com/watch?v=wGGW4IezbC4

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Fight with ARGs

• Financial support from GRF, ECF and ITF of Hong Kong, DSD of Hong Kong government, and NSFC from mainland China.

• Thanks for the collaborators on AMR topics. James Tiedje (MSU), Jim Cole (MSU), Ed Topp (Canada), Amy Pruden (VT), Peter Vikesland (VT), Michael Gillings (Australia), Min Yang (China CAS), Yongguan Zhu (China CAS), Xiangdong Li (PUHK), William Gaze (UK), Pedro Alvarez (Rice U.), Helmut Burgman (Eawag), David Graham (UK), Pascal Simonet (France), Gianluca Corno (Italy), Renata Picao (Brazil), Celia Manaia (Portugal), Keiji Fukuda (HKU), Gloria Dominguez-Bello (New York U.)

• Ph.D. students working on ARGs Bing Li Ying Yang Liping Ma Xiaotao Jiang Xiaole Yin Andong Li Yu Deng Anni Zhang Liguan Li Yu Xia Fegn Ju Lin Ye You Che Yuanqing Chao Lei Liu Yulin Wang

Acknowledgements

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Welcome to Hong Kong! Welcome to EDAR 2019!