El lunes 23 de octubre de 2017 celebramos una jornada en la Fundación Ramón Areces sobre Microbiota Intestinal: Implicaciones en la Salud y Enfermedad.

1. Determinación y caracterización del
microbioma intestinal en el laboratorio
Teresa M. Coque
Instituto Ramón y Cajal de Investigación Sanitaria, IRYCIS
Joint Programming Initiative on Antimicrobial Resistance-SAB
Fundación Ramón Areces
Jornada de Microbiota Intestinal:
Implicaciones en la Salud y Enfermedad
23 Octubre, 2017

2. The Road to Metagenomics: From Microbiology to DNA Sequencing
Technologies and Bioinformatics
Metagenomics timeline and milestones. Timeline showing advances in microbial communities
studies from Leeuwenhoek to NGS
(Escobar-Zepeda et al, Front Genet 2015; Ottman et al., 2012; Yarza et al., 2014).
2001, Microbiome coined by J Lederberg
1996 rRNA database
1989, PCR of rRNA

3. Timeline of microbial community studies using high-throughput
sequencing
shotgun metagenomic projects
Selected representative projects are labeled (open ocean, deep sea, lean mouse, diarrheal
illness, costal ocean, lean/obese gut , human microbiome, MetaHIT (gut) , cow rumen, soil
(NCBI BioProject PRJNA50473), and human gut
Gevers et al, Plos Biology, 2012

4. 4Alivisatos et al; Science 2015; Dubilier et al; Create a global microbiome effort Nature, 2015
Unified Microbiome Initiative (UMI) “ a collaboration
between the scientific community, public, and private sectors
to study global microbiome processes. These partnerships
would allow the development of new technologies that could
accelerate basic discoveries regarding microbiome functions,
the capabilities of the earth’s microbial ecosystems, and
translate them to practical applications. Instead of focusing
on census-taking and descriptive studies driven by genomic
techniques, the emphasis will move to experiments which
establish causal relationships”

5. Precision
Medicine
Predictive, Personalized or Precision
medicine correspond to a field of medicine that
takes into account individual genetic and other
sources of variability in disease treatment and
prevention.
“Precision medicine”(NRC, 2011) is to explore
how treatment or prevention approaches can be
developed based on the combination of genetic,
environmental, and social factors which could be
targeted to individuals or populations.
The concept of “precision medicine” envisioned in
the 2011 NRC report has now been extended to
“precisión public health” which consists on
gaining more insights into complex life course
interactions between biological factors with a
range of personal, environmental and social
determinants of health to measure and track
occurrence of disease in communities and to
implement effective interventions that can
benefit all segments of the population
5
Relative Interest in Precision Medicine (blue) vs.
Personalized Medicine (red), 2005-2016
(Reflected in Google Trend Search on April 10,
2016)

7. 7
Dethlefsen, Nature 2007; Pallen, Nature 2007, Meyer and
Möbius, Fauna der Kieler Bucht. 1865; Ley et al, Nat Micro Rev
2008; Zilber-Rosenberg et al, FEMSMR 2008; Rosenberg et al,
Environm Microbiol 2008; Wilson and Sober, J Theor Biol. 1989
THE HUMAN MICROBIOME

8. 8
Taxonomic distribution, prevalence and abundance of microbial taxa that inhabit
healthy human body sites as defined in the human microbiome projects
(HMP).Belizario and Napolitano; Fontiers Microbiol 2015

9. The Clinical
Microbiologist
in the XXI
Century
Role of the clinical microbiologist (CML)
• Identify bacterial, viral, fungal and parasitic
agents that cause human disease
• Provide diagnostic and therapeutic support
for the clinical management of patients.
• Prevent the transmission of ID in both the
health care system and the community
Changes in technologies and techniques
available to CMLs (syndrome- and disease-
based sampling kits, point-of-care testing, and
isolate typing/characterization by MALDI–TOF
MS or by next-generation sequencing (NGS).
Changes in the classical paradigms; from
descriptive microbiology towards a
cuantitative microbiology
9
The number of identified species (1979-2012)
Founier et al, NMR 2013

10. MICROBIOME ANALYSIS
Technical Background and Approaches
10

11. High-Throughput Metagenomic Technologies
11
Open Formats
• Targeted gene sequencing (TGS,
e.g., 16S rRNA, amoA, nifH)
• Shotgun metagenome sequencing
(SMS)
• Screening for functional
expression
• Metatranscriptome sequencing
(MTS)
• Fingerprinting methods
• Mass spectrometry-based
proteomic and metabolomic
approaches.
Closed Formats
• DNA arrays
• Protein arrays
• Carbohydrate arrays
• Phenotype arrays
• BioLog EcoPlates
• qPCR
• Functional gene arrays (Geochip)
• Phylogenetic arrays (Phylochip)
Zhou et al, mBio 2014
Technologies classified in open and closed formats on the basis that potential
experimental results can or cannot be anticipated prior to performing the analysis

12. Limitations of available methods greatly challenge the sensitivity, the specificity, and
the quantitation possibilities of metagenomic analysis.
Key differences between methods focused of 3 main steps
Zhou et al, mBio 2014
High-Throughput Metagenomic Technologies (2)
In bold, key issues in AbR analysis as we need to detect minority populations and full gene sequences
12

13. Suitability of metagenomic strategies
13
Suitability of metagenomic strategies to analyse different functional traits
Targeted capture approaches (TCP) were developed as the more cost-effective and high-
throughput alternatives overcoming the limitations of WGS and multiplex PCR in tasks
aiming to obtain large data sets of orthologous genes from many individuals
Advantages of TCPs relative to genome- partitioning approaches are scalability, cost-
effectiveness, and enhanced data quality (lower variance in target coverage, more
accurate SNP calling, higher reproducibility and longer assembled contigs).
Jones, Mol Ecol 2015; Olson Nat Methods 2007; Tennesseen et al, Science 2012.

14. 14
21 DNA extraction protocols of the
same faecal material
Comparison extraction quality
• Quantity and quality
• Ratio Gram+ve/Gram –ve
Validation for transferability
(reproductibility)
Quantification accuracy (mock
samples)- Less 0.5x
Workflow of Human Fecal
samples processing
Costea et al, Nat Biotechnol 2017; IHMS website
(http://www.microbiome-standards.org/)

15. 15
Workflow of Human Fecal
samples processing
21 DNA extraction protocols of the
same faecal material
Comparison extraction quality
• Quantity and quality
• Ratio Gram+ve/Gram –ve
Validation for transferability
(reproductibility)
Quantification accuracy (mock
samples)- Less 0.5x
Costea et al, Nat Biotechnol 2017; IHMS website
(http://www.microbiome-standards.org/)

16. Metagenomics: limitations and challenges
Science on gut bacteria still unsettled!!
• Most common technical approaches provide at best species-level
taxonomic resolution, whereas many important phenomena occur
at the strain level (e.g. acquisition of antibiotic resistance genes).
• Most common models for microbiome study design involve cross-
sectional or case-control sampling, but not longitudinal sampling,
and hence fail to capture the dynamic behavior of microbial
communities.
• Fundamental conceptual limitations
– its inability to directly measure the functional activity of a community under a given set
of conditions. Microbiome research: Metagenomic/genomic approaches vs. microbial
biochemistry?? “The Who” vs. “the What” Culturomics: the new challenge?
• Age-old story of correlation vs. causation
– Need to include the comprehensive host analysis
16

17. Culturomics vs Metagenomic methods: Detection
thresholds
Lagier et al, CMI 2012

18. Integrating data Omics for deeper biological insights
Franzosa et al, Nat Microbiol Rev 2015

19. Requirement for Implementation of
Metagenomics Tools
19Zhou et al, mBio 2014
• Minimal processing of the specimen
• Affordable cost
• Hardware and software ready for rapid analysis
of the data
• IT infrastructure to transfer results to clinicians
and Public Health authorities

20. MICROBIOME ANALYSIS
Applications
20

21. From: A Culture-Independent Sequence-Based Metagenomics Approach to the Investigation of an Outbreak of
Shiga-Toxigenic Escherichia coli O104:H4
JAMA. 2013;309(14):1502-1510. doi:10.1001/jama.2013.3231

22. From: A Culture-Independent Sequence-Based Metagenomics Approach to the Investigation of an Outbreak of
Shiga-Toxigenic Escherichia coli O104:H4
JAMA. 2013;309(14):1502-1510. doi:10.1001/jama.2013.3231
Each point on the scatter plot shows
the GC content (x-axis) and total
depth of coverage (y-axis, log10-scale)
colored by taxon for each
environmental gene tag (EGT) in the
outbreak metagenome.
The E coli O104:H4 outbreak genome
reconstruction from environmental gene
tags (EGTs) within the outbreak
metagenome

23. AMR: A major Global Health Challenge
23
• AMR is the ability of microorganisms to resist the action of antimicrobials,
specially of antibiotics
• Expected reduction in population by 2050.
• Impact on ill-health would reduce world economic output between 2%
and 3.5%by 2050

24. Multidisciplinary networks to infer the dynamics of
selection and transmission of AMR genes/clones
(causality and directionality) among different hosts
to design preventive measures and eco-evo
interventions.
To characterize and quantify the processes of
selection for and transmission of AMR genes
and drug-resistant bacteria in complex
(eco)systems from a ‘One Health’ perspective
is a first priority of the scientific community
that require tools enabling the analysis of
complex systems.

25. • What: Single gene/Single strain
• Where: Hospitals
• How: Culturomics
First Era
• What: Resistomes
• Where: Hospitals/Animals/ Environments
• How: Metagenomics/Functional studies
Second Era
• Target: Metagenomes
• Where: hotspots
• How: multidisciplinary approaches
NextEra
Antimicrobial Resistance Research
25
25

26. How characterize the resistome?
Taxonomic profiling (Resistomes and Metagenomes)
Technical approaches
How analyze the resistome?
a- and b -diversity
What does it all mean?
Statistical and network analysis
Causality/directionality/ranking risks

27. What is the Resistome?
Definition and Technical challenges
27

28. The Resistomes
D´Costa et al, Science 2006; Perry et al, COM 2014; Olivares et al, 2013; Martínez et al, Nat Microb Rev 2014
Resistome Pheno
type
Functional Habitat
Acquired Resistome YES YES H+E
Intrinsic Resistome YES YES H+E
Silent Resistome NO YES H+E
Proto- Resistome NO NO E
• A framework to understand and study the origins, evolution and
emergence of resistance (Wright, 2006)
• Genes that cause phenotypic resistance in the clinic and also, those that lie
hidden and silent in the environmental pan-genome.
28

29. Technical Challenges (1) Databases
Crofts et al, Nat Microb Rev 2017; Gibson et al, Nat Microbiol 2016 29
Two human gut resistomes encompassed
greater phylogenetic diversity than all
previously identified classes of β-lactamase
New AMR protein families A conserved genetic
element associated with various rifamycin
antibiotic-inactivating mechanisms.
Cryptic orthologous genes in pathogenic
bacteria (susceptible to the drug)
...you only see what is in the database

30. 1 E. coli ≈ 5 Mb
1 AbR gene ≈ 1 kb
1 AbR gene ≈ 0.02% E. coli genome
50.000.000 reads/sample
1% ≈ 0.1% Proteobacteria
E. coli ≈ 0.1% Metagenome
50 reads/AbR gene in E. coli
Why a Capture Platform?
How many distinct clones of E. coli ??
“One, two, three, or four phylogenetic groups were
simultaneously found in 21%, 48%, 21%, and 8% of the
subjects, respectively” M. smati et al 2013
What might happen with minority populations (i.e Klebsiella sp)?
Enough for
Quantification and/or
Identification??
The math-metagenomic approach

31. NimbleGen SeqCap EZ Platform
SeqCap EZ Library is a solution-based capture method that enables enrichment of customer regions of
interest in a single test tube
It sets a new standard for a simple single-step enrichment method.
31

32. Sequences
(Fasta files)
Blast All-to-All
Similarity Network
Clustering
(MCL)
Multiple Alignment
Phylogenetic analysis HMM profile
MCLMarkow Clustering Algoritm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow
in graph
Profile HMMs have been widely adopted for improved annotation of general functions in microbial genomes and metagenomes
Systematic Analysis of databases (AbR, MetalR, relaxases)
Development a curated DB of protein families and associated profile hidden Markov
models (HMMs), organized by ontology and confirmed for AR function.
AR family profile HMMs were built by
generating a multiple sequence alignment for
each AR family
32

33. HMM Profiles
Non Redundant AbR Database
UniRef100
Manual
Curation
Capture Platform
(SeqCap EZ)
≈81.000 nr genes
Relaxases
Metal &
Biocide
EMBL CDS
ResCap Gene Sequence Capture Platform
47806
30794
2517
7963

34. SeqCap EZ
Sample NGS Library Sequencing
Bioinformatic
Analysis
Workflow OF ResCap_v01
HiSeq 2x100 (1 line)
34

35. Mapping (Bowtie2)
Bioinformatic Analysis
Fastq
Arg-ANNOT BacMet ConjDB
Gene Detection Cluster Count
Abundance Diversity
Reads Per Kilobase Per Million of Reads
(RPKM)
Genes Per Million of Reads
(GPM)
35

36. Considering that the current belief assumes the resistome represents the 0,2% of the intestinal metagenome, we
would need 3.750 millions of reads to reach a similar coverage to that obtained by using the ResCap platform
Two full flow-cell HiSeq2500-Illumina vs 1% of one flow cell of HiSeq 2500 platform used in ResCap (if we consider a
HiSeq 2500 gives 1.500.000.000 reads per flow cell)
• ResCap vs conventional metagenomcis
• On-target 30% (20%-41%) vs 0,11% (0,07%-0,18%) (279 folds (range 480-170 folds)
• Representation of the gain in reads per
kilobase per million of reads (RPKM)
of each detected gene between
conventional sequencing protocol
(abscissa axis) and ResCap (ordinate
axis).
• The pictures are represented in log-log
scale to a better perception of the
linearity of the gain function in genes
detected by both protocols.
ResCap versus Conventional shotgun metagenomics
36

37. Platform Efficiency by Data Base Groups
Data distribution of the platform
efficiency evaluating
(a) the number of mapped reads
per million of sequenced reads
against the three well-known
genes groups
(b) the number of detected genes
per million of sequenced reads
using as reference the three well-
known genes groups

38. Diversity and Abundance of AMRge nes
Abundance (a). It was measured as Read Per Kilobase per Million of reads that mapping against genes or allele-cluster genes of
each antibiotic resistance family
Diversity (b) was measured as number of detected Genes Per Million reads of each antibiotic resistance family

39. Diversity and Abundance of MOB genes
Abundance (a). It was measured as Read Per Kilobase per Million of reads that mapping against genes or
allele-cluster genes of each antibiotic resistance family
Diversity (b) was measured as number of detected Genes Per Million reads of each antibiotic resistance
family

40. Copper (Animal Feed, Antropogenic pollution)
Lanza et al, IMMEM2016; Staehlin et al, GBE 2016
Intracellular Cu stress modulated by both chromosomal and plasmid‐encoded Cu trafficking and export mechanisms
cueO/cueR cusABCFRS cutACEF pcoABCDE tcrB
 Copper Homeostasis and Silver Resistance Island (CHASRI), comprises cusABCFRS and pcoABCDE. Dispersal among Enterobacteriaceae.
Confers Cu tolerance under aerobic and anaerobic conditions. Involved in virulence (Salmonella and Cronobacter)
 tcrB plasmid Enterococcus faecium from animal origin
Plasmid
Chromosome
Cu efflux
(homologs sil)
muticopper
oxidase and P-
type ATPase
Inducible CuR
Homologs cop in
Pseudomonas and
Xanthomonas
Enterobacteraceae
Enterococcus
Zpe efflux pump
(Zn, Fe, Co, Ni, Cu, Cd)
zupT
40

41. Resistome profiles of human and animals
41
ResCapMSS

42. Distribution of genes by host and
method
42

43. 43Lanza et al, Microbiome 2017 (in revision)
The resistome of human and animals by ResCap

44. Pairwise Comparative Modeling (PCM), a
novel 3D method for predicting AMR
44Ruppé et al, 2017 preprint at biorXiv
• A new annotation method based on homology modelling
• It was able to predict 6,095 ARDs (pdARDs) in a 3.9 million protein MetaHit catalogue
• Predicted ARDs (pdARDs) are distantly related to known ARDs (mean aa identity 29.8%).
• 95.6% of pdARD are chromosomal located, Mob genes in 7,9% of pdARD neighbourhood

45.  Largely descriptive
 Correlation with phylogenetic structure of microbial
communities
 Lack of gene-taxon binning
 Synteny of the genetic context differs in different
metagenomes/habitats
Limitations of Resistome Surveys

46. How we analyze the
resistome?
a-diversity and b-diversity
46

47. 47
• Network theory and approaches based on sequence similarity
networks (SSNs) allow studying large-scale evolutionary
relationships, including the influence of habitat and ecology
in the distribution of gene pools, evolution of organisms,
and HGT.
• Metagenomes allows exploring the global distribution of
coding sequences, universally shared phylogenetic marker
genes, and horizontally transferred genes.
Lima-Mendez et al. 2008; Halary et al. 2010; Dagan 2011; Tamminen et al. 2012;
Alvarez-Ponce et al. 2013; Forster et al. 2015, Smillie et al, 2011.
“everything is everywhere but the environment
selects” (Lourens G.M. Baas Becking, 1895-1963)
Spatial Distribution of Microorganisms

48. SSN among the 97 sampling points. Each node = a metagenome project. Each link=the presence of homologous sequences between them.
Node and link sizes, proportional to the number of sequences embedded in the sample and the (normalized) number of shared sequences,
respectively.
Fondi, Karkman, Tamminen, Bosi, Virta, Fani, Alm, O. McInerney. Gen Biol Evol, 2016
Spatial distribution of Metagenomes
Connections among samples form the same (B, C) and different ecological niches (D, E)
339 metagenomes (pooled into roughly 100
sampling points) using an SSN approach
Metagenome gene
composition is strongly
affected by ecology

49. Spatial distribution of Resistomes
• 864 metagenomes from 350 humans, 140 animals and 369 external
environments
• Resistomes and Mobilomes across habitats are highly structured by phylogeny
along ecological gradients
Palet al, Microbiome 2016; Pehrsson et al, Nature 2016

50. 50
Abundance and Diversity of Resistomes (2)
Palet al, Microbiome 2016
Human and animals
• limited taxonomic diversity
• low abundance and diversity
of bc/metRlgenes and MGEs
• High abundance of AbR genes
Environments
• High taxonomic diversity
• High abundance and
diversity of bc/metRlgenes
and MGEs
• VariableAbR genes

51. HGT Network
Force-directed layout
representation of the
metagenome network
Fondi, Karkman, Tamminen, Bosi, Virta, Fani, Alm, O. McInerney. Gen Biol Evol, 2016; Baquero 2008,
Fondi et al, 2010.
90% sequence identity threshold)
Microbial Community Structure. Ecologicalconnectivity(2)
OpportunitiesandBarriersforHGT
Inland water samples occupy a “bridge-like” position in the overall metagenome network
despite maintaining their and connect microbial communities that otherwise would
remain disconnected (e.g., host and seawater samples).

52. 52
Cross habitat HGT events
Smillie et al, 2011
• HGT is ecologically structured by
functional class and at multiple
spatial scales.
• Recent HGT is enriched in the
human microbiome across all
phylogenetic distances
• No direct acquisition

53. 53
AbR gene sharing across the
habitats
Pehrsson et al, Nature 2016
a) Highly cosmopolitan proteins in 21 habitats
b) Protein of AR genes isolated from
functional metagenomic selections vs
genetic contexts

54. 54
Historical background of TEM
Novais et al, AAC 2010: Pehrsson et al, Nature 2016
TEM-1 found in 25 different
genetic context in a recent cross-
habitat resistome study!!!
Only some variants were highly
spread

55. VanA
VanD
VanG
OriginofVanOperons
Guardabassi, FEMS Ecol 2008; Stinear et al, Lancet 2001; Amann et al, Mol Microbiol 2013; Domingo et al. AAC. 2007
van Origin
vanA Bacillus, Paenobacillus
vanB1-3 Clostridium species,
Atopobium, Eggerthella lenta
vanD1-5 Ruminococcus
vanG Ruminococcus, C. difficile
Tree showing the phylogenetic relationships
of the deduced amino acid sequences of the
van-like genes encoding the Ddl, d-Ala-d-
Lac, and d-Ala-d-Ser ligases
NJ 175 postions, bootstrap 1000replications

56. 56
b-diversity of Resistomes and
Mobilomes
Unpublished results (EvoTAR, WP1-WP4) 96 Guyana individuals () and 45 Hospitalized patients at to
AbR Genes Metals/Biocides Genes Relaxases

57. 57
b-diversity of Resistomes and Mobilomes
ResCap of 96 healthy individuals (Guyana) and 45 hospitalized individuals in Bichat)
Unpublished results (EvoTAR, WP1-WP4)
HOSP Guyana

58. 58
AbR Genes Metals/Biocides Genes Relaxases
b-diversity of Resistomes and Mobilomes
(Heatmaps)

59. 59Ruppé et al, 2017 preprint at biorXiv
Distribution of pdARDs in human hosts’ microbiota
MetaHIT cohort (663 subjects). Carriers of pdARDs (0.22%, range 0.14%-0.38%)
• From Tet(M) family (0.07%) to class B3 beta-lactamases (median: 0.004%).
• Unique pdARDs genes per metagenome (median 1,377, range 258-2,367).
• All ARD families with the exception of RNA methylases and AAC(2') families were found
in more than 80% of individuals.
PCA of 663 Metahit subjects of MetaHit cohort, regarding gene richness and resistotypes (A) and enterotypes (B)
R1.VanLigasas
R2. VanLigasas
R3. VanLigasas+TetM+Class C betalac
R4. Tet(X) and class A beta-lactamases
R5/R6. Class B1 beta-lactamases

60. • Identify basic patterns in microbial distributions (abundance and ubiquity)
• Using co-occurrence patterns to help define potentially interacting
organisms and interaction networks in highly complex systems
• Examining seasonally or annually repeating patterns, also leading to
predictions
• Evaluating the roles of viruses (bacteriophages)
• Linking community structure or particular genes to particular functions
Microbial Community Structure. Needs

61. What does it all mean?
Statisticaland network
analysis/Directionality/Ranking risks
61

62. 7 RESistance readiness CONdition (RESCon)

63. Take Home Message
Identification of Risk scenarios is mandatory for establishing effective
surveillance systems/ networks and rank risks. It requires systems-analysis
tools to create
association networks from field data on distributions, and these networks
(eventually with a time component, if the measurements are a time series)
Needs
1. Development of tools (chemical detection, omics/bioinformatics for
minority populations)
2. Multidisciplinary approaches under the perspective of One Health
(composite index, personalized Public Health concepts)
3. International Regulations for the containment of environmental
pollution by pharmaceuticals

64. Next Generation Surveillance
64
• Analysis of hotspots
• Comprehensive Analysis of humans and animals
and factors that contribute to selection
• Development of computational methods
• Improve databases
• Improve probabilistic annotations
• Multifactorial analysis
Baquero, Coque, de la Cruz, AAC 2011; Corona et al, Fut Med Chem 2016

65. Val F. Lanza
Ricardo León-Sampedro
Fernando Baquero
José Luís Martínez
Fernando de la Cruz
María de Toro
Etienne Ruppe
Amine Ghozlane
Dusko Ehrlich
Sean Kennedy
Rob Willems
Willem van Shaick
Bruno González-Zorn
Antoine Andremont
Charles Burdet
Acknowledgements

66. Genetic exchange occurs among Archaea, Proteobacteria and
Eubacteria and is largely indiscriminated with respect to the
specific genes and functional properties
Number and frequency of mobilizations vary among AMR genes
(CTX-M-Class A > Non-CTX-M- Class A> Class C beta-lactamases).
Vancomycin resistance (vanA, vanB>>> van)
Mobilization of widespread AMR genes occurs by widespread
integrases or transposases

67. Bayesian consensus phylogeny of the ampC genes
Barlow & Hall, AAC 2002:46
Using that phylogenetic reasoning, the ampC gene has been mobilized at least six times
Aeromonadaceae (1)
Hafnia alvei (1)
Aeromonas sobria (1)
Enterobacter cloacae (1)
Morganella morganii (2)
Citrobacter freundii (1)
IS186 (FOX-5)
ISA2 (MOX-5)
ISCR1
ISCp1
IS26

68. Bayesian consensus phylogeny of the ampC genes
Barlow & Hall, AAC 2002:46
Using that phylogenetic reasoning, the ampC gene has been mobilized at least six times
Aeromonadaceae (1)
Hafnia alvei (1)
Aeromonas sobria (1)
Enterobacter cloacae (1)
Morganella morganii (2)
Citrobacter freundii (1)
IS186 (FOX-5)
ISA2 (MOX-5)
ISCR1
ISCp1
IS26

69. Barlow et al, EID 2008
Assimilation of blaCTX-M by IS
Phage integrases
ISCR1
ISCp1
IS26

70. Superbugs to kill “more than cancer” by 2050
• Antibiotic Resistance (AbR) is a major cause of illness and death worldwide (10K of dies
by 2050, the costs would spiral to $100tn (£63tn)
• Expected reduction in population and the impact on ill-health would reduce world
economic output by between 2% and 3.5%.
• MDR E. coli, malaria and tuberculosis (TB) would have the biggest impact.
Study based on scenarios modelled by researchers Rand Europe and auditors KPMG 70

71. One Health Approach for Combating AMR
One Health is an approach to ensure the well being of people, animals and the
environment through collaborative problem solving — locally, nationally, and globally.
A worldwide strategy for expanding
interdisciplinary collaborations and
communications in all aspects of
health care for humans, animals and
the environment.
The synergism achieved will advance
health care for the 21st century and
beyond by accelerating biomedical
research discoveries, enhancing
public health efficacy, expeditiously
expanding the scientific knowledge
base, and improving medical
education and clinical care.

72. Global Plans for AMR Containment
WHO. Global Action Plan on AMR (May 2015)
Goal : continuity of successful treatment and prevention of ID Specific
Plans Five objectives. Water, Sanitation and Hygiene (WaSH) within 3/5
Joint Programming Initiative on AMR, Scientific Research Agenda, 2014
22 countries (EU, Canada, Argentina, Israel, Japan)
Six pillars for R&D (2/6 about environment)
ANTRUK Antibiotic Research UK Strategy. The O´Neill report
Specific Plans for animal and environmental dimensions of AMR
Recommendations:
• Reducing antibiotic pollution
• Sanitation, specifically the proper disposal of wastewater at AbR
hotspots
World Bank Group (United Nations) (An Economic Threat, Sep 2016)
• Overview of the fundamental reasons for the importance of AMR to policy-
makers (Economic and development consequences of AMR)
• Findings, options, and recommendations, while recognizing many
knowledge gaps.

73. Fuhrman JA, Nature 2009;
Baas Becking, 1934
“everything is everywhere but the environment
selects” (Lourens G.M. Baas Becking, 1895-1963)
• Large numbers of individually rare species are found in most ecosystems
• Current distributions of organisms are the result of historical factors, including dispersion
by wind, water and animals, and adaptations to local conditions that change over space
and time.
Spatial Distribution of Microorganisms
Rank abundance relationships for bacterial
operational taxonomic units
MICRODIVERSITY
when we look for
DISPERSION

74. b-diversity
74

75. Determinación y caracterización del
microbioma intestinal en el laboratorio
de Microbiología Clínica
Teresa M. Coque
Instituto Ramón y Cajal de Investigación Sanitaria, IRYCIS
Joint Programming Initiative on Antimicrobial Resistance-SAB
Fundación Ramón Areces
Jornada de Microbiota Intestinal:
Implicaciones en la Salud y Enfermedad
23 Octubre, 2017

76. Transmission and Microbial
Inheritance
76
Transmission, Probability of survival to
environment insults and competitiveness
Access, Microbe´s abundance
Resistance, Microbiota composition, niche
quality, host factors
50%50 inherited from parents, also from
close relatives and environment
The majority of gut strains in individuals
are residents for most of their lives!!!
100 species and 200 strains
Faith, PNAS 2014

77. Transmission and Microbial
Inheritance
77
Transmission, Probability of survival to
environment insults and competitiveness
Access, Microbe´s abundance
Resistance, Microbiota composition, niche
quality, host factors
Advances in strain level microbe-typing
technologies improve to enable
longitudinal studies
Need to modify equation by including
terms for the probability of a strain’s
extinction [P(extinctioni)],or to enable
P(transmissioni), P(accessi),and
P(resistancei) of each strain i to change as
a function of time.

78. The Human Microbiome
Structure. Human microbiota is a
metacommunity, comprising site-
specific communities (skin,
mucoses, intestinal lumen) with
strong interactions within each
one and limited interaction or
migration between them.
Composition of human_associated
bacterial communities involves
only 9 phyla, 4 phyla being
dominant (out 50 bacterial phyla
on the Earth). Partner fidelity
feedback
Variability among individuals in
the same habitat. Low at familiy
and genus level; high at strain and
species level.
Dethlefsen, Nature 2007; Pallen, Nature 2007, Meyer and Möbius, Fauna der Kieler Bucht. 1865; Ley et al, Nat Micro Rev 2008;
Zilber-Rosenberg et al, FEMSMR 2008; Rosenberg et al, Environm Microbiol 2008; Wilson and Sober, J Theor Biol. 1989