1. Metagenome: Fungal and Bacterial
interactions
Laurence Delhaes
laurence.delhaes@pasteur-lille.fr
1BDEEP – EA4547 (CIIL), Institut Pasteur de Lille, Université de Lille 2 – France
2Département de Microbiologie, Service de Parasitologie-Mycologie, CHU de Lille - France.

2.  Microorganisms (bacteria, archaea, yeasts, moulds, viruses) are
colonizing all ecological systems
 Such microorganisms are present even in extreme environments
 A majority of these microorganisms remains to be identified
1-4 106 bacteria / g of soil
(tropical rain forest)
2 108 cells / g of soil
(desert)
1,04 1020 cells / cm3 of water
(hypersaline water)
Introduction: Global microbial diversity

3.  Micromycetes: are present in various ecosystems
(but poorly studied/analyzed)
 Playing an important role within soil regeneration
(nutriment - metabolism of plant decomposition)
Of note: Fungi (especially ascomycetes) have/fulfill
along with bacteria a central role in most land-based
ecosystems, as they are important decomposers,
breaking down organic substances.
 1 500 000 represents the number of fungus species
estimated for the entire earth/world
But only 97 000 have been identified
[Hibbett et al. 2007, Mycol Res , 111: 509-547]
Introduction - Microbial diversity: Place of the fungi

4.  As other ecological systems, there is a microbial community /diversity
of human organisms
Introduction: Human Microbial diversity
Species number (bacteria)
Acid mine See Termite hindgut Human gut Soil
Proctor LM (2011) The Human Microbiome Project
in 2011 and Beyond. Cell Host & Microbe 10:287-91
Recently, the Common Fund's Human
Microbiome Project (HMP) has been
developed.
It aims to characterize the microbial
communities found at several different
sites on the human body, including nasal
passages, oral cavities, skin, gastrointestinal
tract, and urogenital tract, and to analyze
the role of these microbes in human
health and disease.

5. The main bacteria isolated in Humans are belonging 4 phyla (among the 50 known phyla). There
are Firmicutes (in blue), Bacteroidetes (in pink), Actinobacteria (in green), and Proteobacteria (in
purple).
http://www.larecherche.fr/content/recherche/article?id=25319
 Human beings: Which bacteria are living in us (The genomes in our genome)?
[La Recherche – a 2011 up-date: 1st panorama drawn from 7 studies realised from 2004 to 2007]
Introduction: Human Microbial diversity
2 Missing
Elements

6.  Respiratory function: A major issue for Public Heath
 In relation with the outdoor environment As for the other
air-breathing animals, human lungs are dealing with gas exchange
(drawing and expulsion of air; 15m3 of air / day / adult; with a fungal
contamination from to 108 to 103 spores/m3 in working to domestic usual
exposure [OMS 2009]
 Lungs: Sterile organs: an old dogma?
[Morris et al. 2013; Beck et al. 2012; Erb-Downward et al. 2011; Huang et al. 2011]
-Respiratory disorders: 1st cause of worldwide consultations
-Chronic obstructive pulmonary disease (COPD): 4th origin in
worldwide decease by 2030 (WHO)
-Cystic Fibrosis (CF): Most common serious hereditary disorder in the
Caucasian population[Rabe et al. « The year of the lung ». Lancet 2010]
Introduction: Human Microbial diversity and Lung

7.  Lung microbial diversity in Cystic Fibrosis (CF):
- Lung diversity = Bacterial microbiota
exists in healthy people [Morris et al. 2013; Beck
et al. 2012; Erb-Downward et al. 2011; Huang et al.
2011]
- This bacterial community has been largely
studied in CF, and seems to be associated
with the evolution of the respiratory function
in CF [Maughan et al. 2012; Guss et al. 2011; van der
Gast et al. 2011; Rogers et al. 2010; Armougom et al.
2009; Bittar et al. 2008; Sibley et al. 2008; Tunney et al.
2008; Harris et al. 2007Goddard et al. 2012; Madan et al.
2012; Fodor et al. 2012]
Introduction: Human Microbial diversity and Lung

8. Purpose: What is the fungal microbiota (or
Mycobiota) of CF patients?
Is the fungal microbiota stable?
Are the mycobiota diversity and
richness associated to the clinical
status of CF patient?
…
What is the fungal composition of lung
microbiota in CF?
 Mycobiota analysis by developping and using high
throughput sequencing approach
Which relation we observed between the
mycobiota and the bacterial composition?
Introduction: Human Microbial diversity and Lung

9. DNA Extraction depends on matrix/substrate
PCRs targeted conserved genes that allow the
amplification of species distant/different
phylogenetically (V3 of 16s rDNA – ITS2)
Massive sequencing (multi-parallelized) – getting
hundreds of thousands of reads
Bio-informatic analysis
Identification by local blast to 2 databases: BLASTN ≠
- Silva SSU rRNA database release 102
- ITS2dbScreen that we designed de novo
Read assignments and clustering
(at the species or genus level)
To allow a biologic analysis of the data,
comparison between samples
(diversity analysis using MEGAN, U-clust, MEGANE5 progamms)
Collectd sputum samples of CF patients
Materials & Methods: Metagenomic approach

10. -Bacteria [16s rDNA region V3] : 326,277
pyrosequences (with 93% : 450-500 bp)
-Fungi [ITS2] : 133,317 pyrosequences
(with 85% : 300-450 bp)
-With adequate rarefaction curves
(confirming we have a good evaluation of
the sample diversity)
Lung mycobiota in CF: Results of the pilot study
 Determine:
• Bacterial diversity (comparable to published data)
• Fungal diversity
 Evaluate the relation between fungal/bacterial community
structure and patient clinical status
8 sputum samples (4 patients) using
454 FLX system

11. Lung mycobiota in CF: Results of the pilot study
 Fungal diversity
- Among the 24 species /genera
identified as fungi using deep-
sequencing, only 4 have been isolated
by cultures.
- Genomic methods allowed the
identification of additional species that
are recognized as microorganisms
involved in respiratory or
infectious diseases
- The median [IQ] number
of microorganism genera
per sputum sample was
3.5 [3; 7.5] micromycetes
[Bouchara et al. 2009].

12. Fungal richness
(Choa1 index)
S-K score= Shwachman-Kulczycki Score (overall clinical
status, activity, lung function, pulnmonary radiography)
Body Mass
Index
(kg/m2)
Lung mycobiota in CF: Results of the pilot study
 Relation between species richness & clinical status

13. Body Mass
Index
(kg/m2)
Lung mycobiota in CF: Results of the pilot study
 Relation between species richness & clinical status
Prokaryote richness
(Choa1 index)
S-K score= Shwachman-Kulczycki Score (overall clinical
status, activity, lung function, pulnmonary radiography)

14. Body Mass
Index
(kg/m2)
Lung mycobiota in CF: Results of the pilot study
 Relation between species richness & clinical status
Choa1 indexes
S-K score= Shwachman-Kulczycki Score (overall clinical
status, activity, lung function, pulnmonary radiography)
Pat. 3 (09/2007)
Pat. 3 (09/2008)
Patients 2
Patient 1 (01/2008)
Patient 1 (01/2009)
Patient 4 (10/2008)
Patient 4 (08/2008)
- Chao1 indexes of fungi
and bacteria are
statistically (p<0.05)
associated with S-K
score and BMI.
- For bacteria, these
results are in agreement
with published data [van
der Gast et al. 2011; Klepas-
Ceraj et al. 2010].

15. % Forced Vital Capacity (FVC)
% Forced Expiratory Volume
(FEV1)
Pat. 3 (09/2007)
Pat. 3 (09/2008)
Patient 1 (01/2008)
Patient 1 (01/2009)
Patient 4 (10/2008)
Patient 4 (08/2008)
Patients 2 (03/2008)
Patients 2 (03/2009)
- Chao1 indexes of
fungi and bacteria are
statistically (p<0.05)
associated with FVC
and FEV1.
Lung mycobiota in CF: Results of the pilot study
 Relation between species richness & clinical status

16.  Validation of the molecular
approach (ITS2 DB+++)
 We observed a decrease of
diversity and richness for
fungal and bacterial
communities significantly
associated with poor clinical
status (S-K score and BMI)
and decreased lung function
(FEV1 and FVC)
 Our results documented the
complexity of fungal and
bacterial communities in CF,
with potential interaction
between species (biofilm)
[Delhaes et al. 2012]
Lung mycobiota in CF: Results of the pilot study

17.  36 sputum samples From patients with (18) and without (18)
pulmonary exacerbation were compared (clinical, radiological,
biological data – 40 variables per patient)
 Microbial analysis is under process:
(i) using deep-sequencing fungal/bacterial analysis
(ii) using RT-PCR targeting RNA respiratory viruses
(iii) using q-PCR targeting DNA respiratory viruses
 Mathematical approach under process
a first PCA (principal component analysis) taking into account the
whole set of variables (40 per patient) for analyzing Mycobiota
versus bacterial microbiota at the genus level - We limited our
analyses to the number of genera that were present at least in 3
patients and the number of OTU present at 1%.
Lung mycobiota in CF: Relevance in CF exacerbation

18. Lung mycobiota in CF: Relevance in CF exacerbation
According to PCA graph:
 Addition of the 2 axes = the
explained part of the variability
→ 33% [42% in Zemanick et al. 2013]
 For each variable, arrow lengh
is proportional to the load of
the corresponding variable on
the first 2 principal
components (Dim/axes 1-2) (the
longer the arrow is = the more the
axes explained the variable)
Our model and axes explained
a lot of microorganisms
Dim2(14.84%)
Dim 1 (18.23%)

19. Lung mycobiota in CF: Relevance in CF exacerbation
Key point to read a PCA graph:
 Interpreting a correlation
between microorganisms as
follow
Dim2(14.84%)
Dim 1 (18.23%)
Right angle =
No correlation
Acute angle =
Positive correlation
180° angle =
Negative correlation

20. Lung mycobiota in CF: Relevance in CF exacerbation
Dim2(14.84%)
Dim 1 (18.23%)
 Pseudomonas
- is alone [Zemanick et al. 2013]
- not correlated with
“Malassezia plus Prevotella
group” [Zemanick et al 2013]
- neither with the “Candida
plus Rothia group” (which is
not well explained by our axes
since the arrows are short)
- but is negatively correlated
with the “group of oral flora
plus some environmental
fungi”, as well as FEV1 –
SK-score[Zemanick et al. 2013]

21. Lung mycobiota in CF: Relevance in CF exacerbation
Dim2(14.84%)
Dim 1 (18.23%)
 Aspergillus
- Unfortunately, our PCA model
doesn’t explained this mold
 Malassezia
- As some anaerobes, M. furfur and
M. sympodialis are difficult to
culture, both obligatory lipophilic,
and skin flora yeasts of humans
Classically, they are associated with
superficial infections of the skin
(pityriasis versicolor - folliculitis)
They appear
+ correlated with
anaerobes in
agreement with
their lipophyly
(since anaerobes
can produce fatty
acids)
ChromAgar
Malassezia
→ Integrate virus data
→ Continue mathematical analysis

22.  Muco-Bac-Myco: Ecology & dynamics of fungal and bacterial communities
of sputum samples from CF patients under antimicrobial treatment: A French
prospective study based on deep-sequencing
Lung mycobiota in CF: To conclude
 Validation of mycobiota analysis based on ITS2 (ITS2DB)
→ Candida et A. fumigatus = Main species/genus
isolated [Charlson et al. 2012; Delhaes et al. 2012]
→ Role in the decrease of pulmonary function
→ Which place for fungi in CF exacerbation?
→ What is mycobiota evolution and role when ATB
treatment are managed? [Muco-Bac-Myco project] - F
Botterel & L Delhaes under process]
 Mycobiota = dynamic event, part of the overall lung microbiome

23.  Determining exhaustively the microbial community
composition in CF patient sputa.
 Developing new approaches based on deep-sequencing,
(standardization)
Larger studies are now required to better understand CF
associated communities
Improving management/survival of CF patients
 Development of ex vivo model biofilm to adapt drug
treatment (anti-bacterial/fungal)
 Predict the efficiency of drug treatment
Lung mycobiota
Improving our knowledge of microbiome by
Lung mycobiota in CF: Perspectives

24. Institut Pasteur-Lille / Université de Lille 2
• Laurence Delhaes
• Eric Viscogliosi
• Eduardo Dei-Cas
• Anne Goffard
• Magali Chabé
Université Littoral Côte d’Opale
• Sébastien Monchy
• Christine Hubans / Stéphanie Ferreira
Faculté de Médecine de Lille
• Benoit Wallaert
• Anne Prévotat
• Julia Salleron
• Fréderic Wallet
• Rodrigues Dessein
• Sylvie Leroy
Société Genoscreen-Lille
Département de Microbiologie
AP-HP Créteil
• Françoise Botterel
• Odile Cabaret
• Jean-Winoc Decousser
• Jean-Philippe Barnier
Consortium Pegase
• Christophe Audebert / Romain Dassonneville
Requiring multidisciplinary approaches
(due to the massive data generated)
Acknowledgments