- Email: [email protected]
Messages from the second International Conference on Clinical Metagenomics (ICCMg2)
Accepted Manuscript Messages from the second International Conference on Clinical Metagenomics (ICCMg2) Etienne Ruppé, Jacques Schrenzel PII:
S1286-4579(18)30049-2
DOI:
10.1016/j.micinf.2018.02.005
Reference:
MICINF 4568
To appear in:
Microbes and Infection
Received Date: 12 January 2018 Accepted Date: 14 February 2018
Please cite this article as: E. Ruppé, J. Schrenzel, Messages from the second International Conference on Clinical Metagenomics (ICCMg2), Microbes and Infection (2018), doi: 10.1016/j.micinf.2018.02.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Messages from the second International Conference on Clinical Metagenomics (ICCMg2) 1
2
Etienne Ruppé , Jacques Schrenzel
1. AP-HP, Hôpital Bichat, Laboratoire de Bactériologie ; INSERM, IAME, UMR 1137 and Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris
RI PT
2. Bacteriology Laboratory, Service of Laboratory Medicine, Department of Genetics and Laboratory
SC
Medicine, Geneva University Hospitals, 4 rue Gabrielle-Perret-Gentil, 1205 Geneva, Switzerland.
*Corresponding author
Laboratoire de Bactériologie Hôpital Bichat-Claude Bernard 46 rue Henri Huchard 75018 Paris, France
Tel +33 140258503 Fax +33 140258580
TE D
[email protected]
M AN U
Etienne Ruppé
AC C
EP
Keywords: metagenomics; next-generation sequencing; microbiota; bioinformatics; conference report
ACCEPTED MANUSCRIPT Abstract Clinical metagenomics (CMg) refers to as the application of metagenomic sequencing of clinical samples in order to recover clinically-relevant information. Due to the increasing access to nextgeneration sequencing (NGS) facilities, CMg is a gast-growing field. In this context, we held the second International Conference on Clinical Metagenomics (ICCMg2) in Geneva in October 2017.
RI PT
During the two days of the conference, several aspects of CMg were addressed, which we propose to summarize in the present manuscript. Besides, we also discuss the evolution of CMg from the last conference held in 2016. In brief, many technical issues related to CMg are expected to be
SC
successfully addressed in the coming years. Conversely, assessing the clinical value of CMg,
AC C
EP
TE D
M AN U
implementing a quality process, storage of data and the ethics of CMg are emerging challenges.
ACCEPTED MANUSCRIPT
Introduction Clinical metagenomics, referred to as the application of next-generation sequencing to clinical
RI PT
samples in order to recover information of clinical relevance, is a fast-moving field standing at the intersection of clinics, microbiology and bioinformatics. On October 19 and 20, 2017, we held the second International Conference on Clinical Metagenomics (ICCMg2, Figure 1) at the Campus
SC
Biotech in Geneva, Switzerland. Twenty-three talks (Table 1) were delivered to approximately 170 people coming from 20 countries (Figure 2). The conference was divided in five sessions. First, we held an update session on next-generation sequencing (NGS) methods, metagenomics and
M AN U
databases. Then, speakers from various settings (private, academic and regulatory agency) shared their experience and thoughts about the validation of NGS-based tests as routine diagnostic tests. As for ICCMg1, the main part of the presentations was dedicated to applications of clinical metagenomics. Eventually, we held a session on the clinical impact of microbiota studies and one session on antibiotic resistance. In the present article, we aimed at coming back to some key
TE D
messages from ICCMg1 [1] and discuss the extent of changes observed during the past year (Figure 3, Table 2). Besides, new issues have been put forward during the ICCMg2 discussions, which we report here. Hence, this yearly report aims at being a snapshot of where stands clinical metagenomics
EP
at the moment. Of note, most of the ICCMg2 presentations (as well as from those from ICCMg1) are
AC C
available at www.clinicalmetagenomics.org in the archives section.
1. What did we achieve since ICCMg1? Microbiome studies
Since the development of next-generation sequencing and the establishment of gene catalogues from the intestinal microbiota by the MetaHIT [2,3] and Human Microbiome Project (HMP) [4] consortia, several case-control studies aiming at assessing links between the composition of the intestinal microbiota and diseases have been published, said O. Pedersen. While many of them have indeed shown correlation between a specific composition of the intestinal microbiota (i.e. the over/under representation of some species) in subjects with a specific condition when compared to healthy
ACCEPTED MANUSCRIPT subjects, O. Pedersen stressed that correlation is not causation. Such case-control studies raise the chicken and egg issue: does a specific composition of the intestinal microbiota lead to clinical consequences, or does a medical condition alter the composition of the intestinal microbiota? O. Pedersen suggested to implement prospective microbiome studies (before the medical condition occurs) to perform intervention (e.g. lifestyle interventions to assess possible changes in the intestinal
RI PT
microbiota or conversely, perform faecal microbiota transplantation [FMT] to assess possible improvements in the medical condition), and whenever possible to include subjects at various stages of the disease (at-risk subjects, pre-disease subjects, subjects with disease) and to perform in vitro/in
SC
vivo (e.g. murine model) experiments. Likewise, P. Veiga highlighted the importance of a critical reading of analyses and results of microbiome case-control studies, stressing the importance of a biological/clinical expertise along with the bioinformatic and biostatistical analysis. Finally, M. Dadlani
M AN U
introduced the metagenomic pipeline used by CosmosID and underlined the need for identifying bacteria beyond the species level, as it was pointed last year with the Peer Bork presentation [1]. For example, Escherichia coli can be either an enterohaemorragic pathovar or a simple, harmless commensal.
TE D
The importance of contaminants in clinical metagenomics This issue was raised again in several talks and all clinical metagenomics workflows presented during ICCMg2 included negative control(s). Remaining issues are to define the appropriate negative
EP
control(s). S. Miller uses the elution buffer as negative control, as many do. Interestingly, he also uses “swipe tests” consisting in sampling the environment surrounding the sample preparation in order to
AC C
broaden the range of detection of possible contaminants outside the reagents themselves. Besides, R. Patel warned against the many contaminants brought by whole genome amplification (WGA) kits [5]. Indeed, in her findings, the microbial composition of prosthetic joint infection samples clustered according to the WGA kits, and not according to the samples themselves as it would be expected.
The bottleneck of DNA extraction and the universal pipeline One of the key issues in clinical metagenomics is to remove the host DNA. In this perspective, most speakers in ICCMg2 reported specific methods to deplete human DNA. J. O’Grady and S. Hauser showed very efficient methods that removed up to 99.99% human DNA without substantial loss of
ACCEPTED MANUSCRIPT bacterial DNA, but their protocols could not be detailed as patents were being filed. R. Patel reported that better results in human DNA removal on prosthetic joint samples were achieved using the Molysis
TM
®
®
enrichment kit (Molzym ) when compared to the NEBNext Microbiome Enrichment kit ®
(NEBNext ) [6]. Moreover, the trend towards a universal clinical metagenomic test already raised during ICCMg1 continued with ICCMg2. Hence, in order to detect RNA viruses at least, RNA needs to
RI PT
be extracted and cDNA sequenced (such as in the workflow presented by C. Chiu). During the RNA extraction, no human RNA depletion is currently used, so that the expression of the host could be analysed in parallel, as it has recently been reported in respiratory samples [7]. Indeed, provided an
SC
acceptable depth of sequencing, taking the host’s response in account together with the finding of microbes of uncertain pathogenicity could improve our interpretation of clinical metagenomic analysis. C. Chiu presented preliminary results from a machine-learning based study. He sequenced the cDNA
M AN U
(host expression) of 25 cerebrospinal fluids (CSF) with bacterial infections and 48 with viral infections as a training set and used 6 and 8 CSF with respective bacterial and viral infections as validation set. Despite a limited number of samples, the support vector machine (SVM) was able to discriminate bacterial from viral infections with 90% accuracy, suggesting the high potential of such approaches in
TE D
clinical metagenomics.
The increasing speed of clinical metagenomics
Last year, sequencing was being accelerated by using single-end, shorter reads options in Illumina ®
EP
sequencers or Nanopore sequencing. While no major breakthrough in the speed of Illumina
sequencers was reported during ICCMg2, several authors shared their experience with Nanopore ®
AC C
(Oxford Nanopore Technologies ) sequencing. C. Chiu showed examples of the application of Nanopore sequencing, including the sequencing of various clinical samples (broncho-alveolar fluids, blood, joint fluids). In less than three hours after the sample was collected, reads from the pathogen (spanning bacteria, viruses and parasites) could be detected for 11 out of 13 samples. Of note, three of these samples were analysed on-site in the Democratic Republic of Congo and in Mexico, supporting the delocalized use of clinical metagenomics offered by Nanopore sequencing. K. Schmidt showed the results of urine clinical samples (with a high load of bacterial cells) sequencing with Nanopore MinION with a four-hour turn-around time [8]. Importantly, the quality of reads increased along with the provision of new flow cells, but multiple alleles of the same antibiotic resistance gene
ACCEPTED MANUSCRIPT ®
(ARG) were still detected with the Nanopore MinION where only one gene was identified by Illumina
sequencing. J. O’Grady showed some preliminary results of the INHALE study in which 600 ®
respiratory specimens were sequenced by the Nanopore MinION. The average turn-around time (from samples to results) was eight hours (using the Rapid low input kit), which could be further reduced to five to six hours in the near future. Of note, the estimated cost per sample was 120 USD.
RI PT
Besides, like sequencing going faster and faster, bioinformatic tools are continuously improved for their performance and fastness, and it is quite hard for the user to have access to solid benchmark in order to choose the right tool for the right indication. In this perspective, the Critical Assessment of
“New” culprits identified by metagenomic studies
SC
Metagenome Interpretation (CAMI) initiative presented by A. McHardy provided valuable insights [9].
M AN U
From the sequencing of 408 joint fluid samples (195 being from aseptic failures and 213 from infections), R. Patel showed that metagenomic sequencing could identify bacteria missed by conventional methods in PJI, as previously showed in bone and joint infections by our group [10]. Particularly, she reported the finding of Mycoplasma salivarium found only by the metagenomic
A2058T in the rRNA gene.
TE D
sequencing of a PJI sample [11]. This strain was predicted to be resistant to macrolides due to a
Differentiating pathogens from harmless bacteria in clinical metagenomics shall be more complex than it was with conventional methods. P. Gyarmati showed that blood samples from healthy subjects
EP
could contain some bacterial DNA [12]. In his work, P. Gyarmati observed that patients had a higher diversity of bacterial species (as detected by shotgun metagenomic sequencing) when they were
AC C
neutropenic than when they recovered from neutropenia. C. Chiu reported cases where clinical metagenomics could identify a microbial pathogens while conventional methods failed: a case of a 58 year-old woman being contaminated after a lung transplant by the hepatitis E virus [13], a case of a couple on honeymoon returning from Maui with an infection caused by a worm, a fatal case of Saint-Louis encephalitis virus [14] and several other cases where microbes were identified in clinical metagenomics only. V. Küfner (awardee of the first ICCMg/SSM prize) reported the transmission of the human JC polyomavirus (JCPyV) from donor to recipient during kidney transplantation. The pathogenic role of JCPyV in such context is unknown and this virus is currently not part of the routine viral screening test of the donors. S.J. Pamp showed
ACCEPTED MANUSCRIPT results about the application of clinical metagenomics in infective endophtalmitis [15]. She reported from 14 patients a good concordance of culture and sequencing despite the absence of human DNA depletion. Of note, several negative controls were used. C. Rodriguez reported the use of clinical metagenomics in dermohypodermitis and necrotizing fasciitis in comparison to the 16S profiling and culture. Interestingly, metagenomic sequencing consistently found more microbes than 16S profiling.
RI PT
Still in some cases, bacteria identified in culture could not be detected by metagenomic sequencing. Eventually, R. Schlaberg reported that putative pathogens could be detected in 30% of children with
SC
pneumonia for which conventional testing returned negative [16].
New messages from ICCMg2 Storage and ethics
M AN U
During the first talk of ICCMg2, L. Farinelli (a pioneer of next-generation sequencing and co-inventor with Pascal Mayer of the DNA colonies) presented the tremendous evolution of sequencing from the ®
®
TM
first Solexa devices from the new Illumina NovaSeq
system. In this perspective, L. Farinelli raised
the issue of data storage. Clinical metagenomics requires a high number of reads in order to detect putative subdominant microbial population(s) and their antimicrobial resistance determinants.
TE D
Moreover, as clinical metagenomics is moving to universal pipeline, both DNA and cDNA should be sequenced. Consequently, the weight of the fastq files obtained from the sequencers is expected to grow bigger and bigger. Hence, should we keep the raw data and pay for the cost of storage, or did
EP
we already reach a point where re-sequencing is cheaper than storage? Will regulatory agencies require the storage of fastq files but also intermediate files (as some bioinformatic tools do not
AC C
produce the exact same results even when applied to the same material) for traceability? L. Farinelli also raised the points of ethics in sequencing. Deep sequencing of samples could bring out information that was not primarily investigated. Hence, should the patient or the doctor access the “unwanted” information or will we keep a right of not knowing it? L. Farinelli concluded his speech in stressing the importance of applying NGS for the good of humankind and not for profit.
Importance of quality Quality in clinical metagenomics was chosen to be discussed in ICCMg2 during a dedicated session, but several speakers also addressed the quality issue during their talks. P. Beurdeley detailed the
ACCEPTED MANUSCRIPT steps leading to the CE marking of the iDTECT
TM
test, in compliance with the European directive
98/79/EC. From the academic side, S. Miller presented the implementation of clinical metagenomics with respect to the Clinical Laboratory Improvement Amendments (CLIA) with a special emphasis on quality control (QC) and quality assurance (QA). S. Miller reviewed the different QC methods: reagent QC, external and internal controls, sample processing controls (SPC) and the control of
RI PT
contamination. He shared the experience of his laboratory about the identification of critical reagents that need to be validated in the laboratory prior to use (such as enzymes). As for external controls, his lab has set up a consortium of DNA virus (CMV), RNA virus (HIV), bacteria (Klebsiella pneumoniae,
SC
Streptococcus agalactiae), fungi (Aspergills niger), yeast (Cryptococcus neoformans) and a parasite (Toxoplasma gondii) as a positive control, and uses the elution buffer (of the DNA extraction kit) as a negative control. Besides, he uses two phages (the RNA MS2 phage and the DNA T1 phage) as
M AN U
internal controls for the whole process. As for identifying true microbial DNA in the sample from contamination, he uses a threshold of 10x the number of reads of the contaminant when present. Aside from the sequencing of the elution buffer, S. Miller also implemented the realisation of “swipe tests” aiming at spanning the environmental microbiome surrounding the whole clinical metagenomics process. All the microbial species found in the negative controls are stored in a dedicated database
TE D
aiming at tracking contamination over time. From the regulatory agencies point of view, K. Roth exposed the process required by the US Food and Drugs Administration (FDA) for a new test to obtain clearance. K. Roth highlighted the many opportunities offered by clinical metagenomics i.e. the
EP
possibility of identifying fastidious organisms, identify common microbes in atypical infections or uncommon microbes in common infections, and to increase the diagnostic yield in challenging clinical
AC C
syndromes such as pneumonia. Nonetheless, the validation of performances and the reporting of results creates a challenging issue for regulatory agencies. K. Roth showed the example of mass spectrometry which was recently introduced in the routine microbiology laboratories. In mass spectrometry, bacteria and yeasts are identified using a score reflecting the degree of confidence of the measure. Similarly, the identification of microbes by clinical metagenomics could be supported by a confidence score that could possibly be based on the abundance of reads (in comparison with that from negative controls), the extent of the genome coverage of reference species (e.g. the presence of reads covering 95% of the genome of Staphylococcus aureus with a 10x coverage would indicate with a high confidence that S. aureus was actually present in the sample) together with satisfactory results
ACCEPTED MANUSCRIPT for internal controls. In this perspective, S. Hauser showed some reports from direct sequencing of respiratory samples that could be used as examples for building clinical reports of clinical metagenomics. Eventually, K. Roth insisted on the clinical validation of NGS-based tests as they may identify microbes for which clinical relevance remains to be determined.
RI PT
Resistance
M. Ellington reported the work undertaken by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) subcommittee on the role of whole genome sequencing (WGS) in antibiotic
SC
susceptibility testing (AST). Indeed, assessing the link between the genotype and the phenotype at the genomic level is essential to move on to the metagenomic level (which raises other difficulties such as connecting resistance genes to their actual host). The issue of the ARG database remains,
M AN U
such as highlighted by P. Lemercier during his talk about databases. Several have been published but none has been qualified for clinical purpose. Moreover, to date none includes clinically-relevant metadata such as the antibiotics to which the ARG confers resistance to, the usual host and the location on mobile genetic elements. Eventually, such a database should be sustained and continuously updated since new ARGs are continuously described, most being alleles of known ARGs
TE D
but some being new such as the plasmid-encoded colistin-conferring resistance Mcr [17]. During their talks, O. Barraud and G. Dantas showed the potential of metagenomics to uncover the diversity of resistance genes from various environments. Such studies are crucial to understand how resistance
EP
genes circulate outside healthcare structures. Besides, newly identified resistance genes should be included in the clinical ARG database in order to increase the exhaustiveness and to be able to
AC C
identify the presence of unexpected ARG in pathogens. As of today, knowing the AST profile of bacteria drives the antibiotic regimen in that ineffective therapy increases the risk of clinical failure. On the other hand, the administration of a proper antibiotic regimen does not guarantee the clinical success, so that other bacterial or hosts determinants not directly involved in antibiotic resistance can be involved. While conventional AST indeed assesses whether a bacterium is susceptible or resistant to an antibiotic in the context of infection, clinical metagenomics could offer complementary information about not only antibiotic resistance but clinical resistance, defined as the capacity of an infection to persist despite an appropriate treatment. Possibly, some markers in the host response and bacterial virulence factors
ACCEPTED MANUSCRIPT could be identified by clinical metagenomics as associated to clinical resistance, shifting our understanding of microbiological testing. As M. Ellington mentioned during his talk: “a minimal inhibitory concentration (MIC) reflects more than gene presence / absence”. We could add that the clinical prognosis reflects more than an MIC, and this part could possibly be addressed by clinical
RI PT
metagenomics.
Perspectives
The second ICCMg confirmed that clinical metagenomics is attracting more and more attention from
SC
clinicians, microbiologists and bioinformaticians. The presentations given during ICCMg2 were more diverse than last year (with a special mention to F. Maixner who introduced paleo-clinical metagenomics) in that they covered more subjects and originated from more groups than for ICCMg1.
M AN U
More than ever, we feel that ICCMg shall accompany the clinical metagenomics wave in being the place for discussion and debate for people aiming at implementing clinical metagenomics. Accordingly, ICCMg3 will be organized next October 18-19, 2018 in Geneva.
Acknowledgements
TE D
We would like to thank the speakers who accepted to participate to the ICCMg2, the people who attended the conference and the sponsors (the American Society of Microbiology [ASM], Campus Biotech Foundation, the Federation of European Microbiology Societies [FEMS], Geneva University
EP
Hospitals [HUG], Swiss National Fund 31CO30_175439, Becton-Dickinson, Danone, Ofxord Nanopore Technologies, Roche, bioMérieux, Fasteris, Ferring, Institut Mérieux, MaaT Pharma, Nestlé
AC C
and Pathoquest) who supported the event. We are also grateful to the Campus Biotech Foundation (Ms Isabelle Bonne and Mr Benoît Dubuis) who kindly welcomed us, the Geneva University Hospitals (Ms Serena Baldelli) for the communication (program and leaflets), Julien Gregorio for the pictures taken during the conference, Mr Sylvain Morin (Concrétisateur) for the website and Kuoni Destination Management (Ms Ségolène Boudou and Mr Huw Francis) for their major contribution to the organization of this conference. Finally, we would like to give a very special thanks to Mr Enrico Zuffi and his team (Bijan Ali Hamouda and Lisa Zuffi) for their tremendous work and efforts for holding this second ICCMg.
ACCEPTED MANUSCRIPT References [1]
Ruppé E, Greub G, Schrenzel J. Messages from the first International Conference on Clinical Metagenomics (ICCMg). Microbes Infect 2017;19:223–8.
[2]
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010;464:59–65. Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, et al. An integrated catalog of reference
RI PT
[3]
genes in the human gut microbiome. Nat Biotechnol 2014;32:834–41. [4]
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human
[5]
SC
microbiome. Nature 2012;486:207–14.
Thoendel M, Jeraldo P, Greenwood-Quaintance KE, Yao J, Chia N, Hanssen AD, et al. Impact
M AN U
of contaminating DNA in whole-genome amplification kits used for metagenomic shotgun sequencing for infection diagnosis. J Clin Microbiol 2017;55:1789–801. [6]
Thoendel M, Jeraldo PR, Greenwood-Quaintance KE, Yao JZ, Chia N, Hanssen AD, et al. Comparison of microbial DNA enrichment tools for metagenomic whole genome sequencing. J Microbiol Methods 2016;127:141–5.
[7]
Langelier C, Zinter MS, Kalantar K, Yanik GA, Christenson S, O’Donovan B, et al. Metagenomic
TE D
sequencing detects respiratory pathogens in hematopoietic cellular transplant patients. Am J Respir Crit Care Med 2017. doi:10.1164/rccm.201706-1097LE. [Epub ahead of print] [8]
Schmidt K, Mwaigwisya S, Crossman LC, Doumith M, Munroe D, Pires C, et al. Identification of
EP
bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicrob Chemother 2017. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical Assessment
AC C
[9]
of Metagenome Interpretation-a benchmark of metagenomics software. Nat Methods 2017;14:1063–71.
[10] Ruppé E, Lazarevic V, Girard M, Mouton W, Ferry T, Laurent F, et al. Clinical metagenomics of bone and joint infections: a proof of concept study. Sci Rep 2017;7:7718. [11] Thoendel M, Jeraldo P, Greenwood-Quaintance KE, Chia N, Abdel MP, Steckelberg JM, et al. A possible novel prosthetic joint infection pathogen, Mycoplasma salivarium, identified by metagenomic shotgun sequencing. Clin Infect Dis 2017;65:332–5.
ACCEPTED MANUSCRIPT [12] Nikkari S, McLaughlin IJ, Bi W, Dodge DE, Relman DA. Does blood of healthy subjects contain bacterial ribosomal DNA? J Clin Microbiol 2001;39:1956–9. [13] Murkey JA, Chew KW, Carlson M, Shannon CL, Sirohi D, Sample HA, et al. Hepatitis E virusassociated meningoencephalitis in a lung transplant recipient diagnosed by clinical metagenomic sequencing. Open Forum Infect Dis 2017;4:ofx121.
RI PT
[14] Chiu CY, Coffey LL, Murkey J, Symmes K, Sample HA, Wilson MR, et al. Diagnosis of fatal human case of St. Louis encephalitis virus infection by metagenomic sequencing, California, 2016. Emerg Infect Dis 2017;23:1964–8.
SC
[15] Kirstahler P, Bjerrum SS, Friis-Moller A, Cour M la, Aarestrup FM, Westh H, et al. Genomicsbased identification of microorganisms in human ocular body fluid. BioRxiv 2017:176529. [16] Schlaberg R, Queen K, Simmon K, Tardif K, Stockmann C, Flygare S, et al. Viral pathogen
M AN U
detection by metagenomics and pan viral group PCR in children with pneumonia lacking identifiable etiology. J Infect Dis 2017;215:1407–15.
[17] Liu Y-Y, Wang Y, Walsh TR, Yi L-X, Zhang R, Spencer J, et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological
AC C
EP
TE D
and molecular biological study. Lancet Infect Dis 2016;16:161–8.
ACCEPTED MANUSCRIPT Legends Figure 1: Logo of the second International Conference on Clinical Metagenomics (ICCMg2).
Figure 2: Pictures from the conference. A: The audience and C. Rodriguez delivering his talk. B: The audience and C. Chiu delivering his talk.
C: Interactions during the poster session. D: The
by J. Schrenzel and E. Ruppé.
Figure 3: Main messages from ICCMg2
M AN U
Table 1: Speakers and titles of the talks delivered at ICCMg2.
SC
RI PT
ICCMg/SSM prize awarded to Ms Verena Küfner and the ASM prize awarded to Ms Mónika Számel
Table 2: Summary of the take-home messages and related key-points of the ICCMg2. CLIA: Clinical
AC C
EP
TE D
Laboratory Improvement Amendments. ISO: International Organization for Standardization.
Speaker Laurent Farinelli Update in sequencing Alice McHardy and bioinformatics Philippe Lemercier Pascale Beurdeley Regulatory issues raised Steve Miller by clinical metagenomics Kristian Roth Charles Chiu Justin O'Grady Christophe Rodriguez Manoj Dadlani Verena Kufner Robin Patel Clinical metagenomics Katarzina Schmidt Sébastien Hauser Sünje Johanna Pamp
Salt Lake City (United States)
Frank Maixner
Bolzano (Italy) Saint-Louis (United States)
Matthew Elligton Olivier Barraud Oluf Pedersen Peter Gyarmati Patrick Veiga
London (United Kingdom) Limoges (France) Copenhagen (Denmark) Peoria (United States) Palaiseau (France)
Title of the presentation From yesterday to tomorrow: past, present and future of sequencing Critical Assessment of Metagenome Interpretation On the importance of databases Experience of the development of iDTECT, the first NGS based CE-IVD test for pathogen detection Experience from an academic: QC in clinical metagenomics pipeline Potential approaches to validation of sequencing based assays (video-conference) Clinical metagenomics: our real-life experience Rapid metagenomic diagnosis of pneumonia Clinical metagenomics of dermohypodermitis Metagenomics for rapid pathogen detection and characterization of the microbiome in health and disease Virus transmission during kidney transplantation assessed by virome analysis of living donor and recipient Clinical metagenomics in bone and joint infections Nanopore sequencing of urine samples Clinical metagenomics of hospital-acquired pneumonia Clinical metagenomics in endophthalmitis Detection of previously missed pathogens in immunocompromised children with suspected pulmonary infections by a fully-validated metagenomics-based test Clinical metagenomics applied to human mummifed remains – Reconstruction of a 5,300-year-old Helicobacter pylori genome from the Iceman’s stomach Understanding and combatting resistome exchange across commensal, environmental, and pathogenic microbes Wrap up of the NGS/EUCAST consultation NGS for characterization of clinical class 1 integrons from hospital effluents So what? Clinical impact of microbiome studies The blood microbiome in health and disease Metagenomics: think before you speak
TE D
M AN U
SC
Gautam Dantas
EP
Microbiota
Robert Schlaberg
AC C
Antibiotic resistance
ACCEPTED MANUSCRIPT
City (Country) Geneva (Switzerland) Dusseldorf (Germany) Geneva (Switzerland) Paris (France) San Francisco (United States) Rockville (United States) San Francisco (United States) Norwich (United Kingdom) Créteil (France) Rockville (United States) Zurich (Switzerland) Rochester (United States) Norwich (United Kingdom) Grenoble (France) Lyngby (Denmark)
RI PT
Session
ACCEPTED MANUSCRIPT
Message
Key points
Push the identification of bacteria up to the strain level. Case-control studies: towards more complex design to address causality. Importance of a biological/clinical expertise along with the bioinformatic and biostatistical analysis What negative control(s) should be used? The importance of contaminants in clinical metagenomics How to substract the contaminants from the results? Consider viruses (DNA and RNA), bacteria, antibiotic resistance genes, fungi, parasites in a single pipeline. Extract DNA and RNA. Towards a universal pipeline and consequences on the nucleic acids extraction. Consider the host's gene expression. Several efficient solutions (most unpublished yet) to remove human DNA. Fast results within hours with Nanopore sequencing, yet quality still not optimal. The increasing fastness of clinical metagenomics Pathogenicity of unexpected microbes? “New” culprits identified by metagenomic studies Already actionable results when conventional methods fail to identify any causative microbe. Microbiome studies
Adapt CLIA or ISO15189 requirements to the clincal metagenomics workflow. Validation of the method: towards a confidence score (like mass spectrometry?) Are clinical parameters the best comparator to validate clinical metagenomics tests? EUCAST consultation: the WGS antibiogram not for now, but works well for some couples bacterium-antibiotic. Metagenomics allows to identify new resistance genes. Need for a database of resistance genes and associated metadata. Towards a clinical resistance with clinical metagenomics instead of an antimicrobial resistance?
RI PT
Quality
AC C
EP
TE D
M AN U
SC
Antibiotic resistance
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
A
EP AC C
C
TE D
M AN U
SC
RI PT
B
D
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
S1286-4579(18)30049-2
DOI:
10.1016/j.micinf.2018.02.005
Reference:
MICINF 4568
To appear in:
Microbes and Infection
Received Date: 12 January 2018 Accepted Date: 14 February 2018
Please cite this article as: E. Ruppé, J. Schrenzel, Messages from the second International Conference on Clinical Metagenomics (ICCMg2), Microbes and Infection (2018), doi: 10.1016/j.micinf.2018.02.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Messages from the second International Conference on Clinical Metagenomics (ICCMg2) 1
2
Etienne Ruppé , Jacques Schrenzel
1. AP-HP, Hôpital Bichat, Laboratoire de Bactériologie ; INSERM, IAME, UMR 1137 and Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris
RI PT
2. Bacteriology Laboratory, Service of Laboratory Medicine, Department of Genetics and Laboratory
SC
Medicine, Geneva University Hospitals, 4 rue Gabrielle-Perret-Gentil, 1205 Geneva, Switzerland.
*Corresponding author
Laboratoire de Bactériologie Hôpital Bichat-Claude Bernard 46 rue Henri Huchard 75018 Paris, France
Tel +33 140258503 Fax +33 140258580
TE D
[email protected]
M AN U
Etienne Ruppé
AC C
EP
Keywords: metagenomics; next-generation sequencing; microbiota; bioinformatics; conference report
ACCEPTED MANUSCRIPT Abstract Clinical metagenomics (CMg) refers to as the application of metagenomic sequencing of clinical samples in order to recover clinically-relevant information. Due to the increasing access to nextgeneration sequencing (NGS) facilities, CMg is a gast-growing field. In this context, we held the second International Conference on Clinical Metagenomics (ICCMg2) in Geneva in October 2017.
RI PT
During the two days of the conference, several aspects of CMg were addressed, which we propose to summarize in the present manuscript. Besides, we also discuss the evolution of CMg from the last conference held in 2016. In brief, many technical issues related to CMg are expected to be
SC
successfully addressed in the coming years. Conversely, assessing the clinical value of CMg,
AC C
EP
TE D
M AN U
implementing a quality process, storage of data and the ethics of CMg are emerging challenges.
ACCEPTED MANUSCRIPT
Introduction Clinical metagenomics, referred to as the application of next-generation sequencing to clinical
RI PT
samples in order to recover information of clinical relevance, is a fast-moving field standing at the intersection of clinics, microbiology and bioinformatics. On October 19 and 20, 2017, we held the second International Conference on Clinical Metagenomics (ICCMg2, Figure 1) at the Campus
SC
Biotech in Geneva, Switzerland. Twenty-three talks (Table 1) were delivered to approximately 170 people coming from 20 countries (Figure 2). The conference was divided in five sessions. First, we held an update session on next-generation sequencing (NGS) methods, metagenomics and
M AN U
databases. Then, speakers from various settings (private, academic and regulatory agency) shared their experience and thoughts about the validation of NGS-based tests as routine diagnostic tests. As for ICCMg1, the main part of the presentations was dedicated to applications of clinical metagenomics. Eventually, we held a session on the clinical impact of microbiota studies and one session on antibiotic resistance. In the present article, we aimed at coming back to some key
TE D
messages from ICCMg1 [1] and discuss the extent of changes observed during the past year (Figure 3, Table 2). Besides, new issues have been put forward during the ICCMg2 discussions, which we report here. Hence, this yearly report aims at being a snapshot of where stands clinical metagenomics
EP
at the moment. Of note, most of the ICCMg2 presentations (as well as from those from ICCMg1) are
AC C
available at www.clinicalmetagenomics.org in the archives section.
1. What did we achieve since ICCMg1? Microbiome studies
Since the development of next-generation sequencing and the establishment of gene catalogues from the intestinal microbiota by the MetaHIT [2,3] and Human Microbiome Project (HMP) [4] consortia, several case-control studies aiming at assessing links between the composition of the intestinal microbiota and diseases have been published, said O. Pedersen. While many of them have indeed shown correlation between a specific composition of the intestinal microbiota (i.e. the over/under representation of some species) in subjects with a specific condition when compared to healthy
ACCEPTED MANUSCRIPT subjects, O. Pedersen stressed that correlation is not causation. Such case-control studies raise the chicken and egg issue: does a specific composition of the intestinal microbiota lead to clinical consequences, or does a medical condition alter the composition of the intestinal microbiota? O. Pedersen suggested to implement prospective microbiome studies (before the medical condition occurs) to perform intervention (e.g. lifestyle interventions to assess possible changes in the intestinal
RI PT
microbiota or conversely, perform faecal microbiota transplantation [FMT] to assess possible improvements in the medical condition), and whenever possible to include subjects at various stages of the disease (at-risk subjects, pre-disease subjects, subjects with disease) and to perform in vitro/in
SC
vivo (e.g. murine model) experiments. Likewise, P. Veiga highlighted the importance of a critical reading of analyses and results of microbiome case-control studies, stressing the importance of a biological/clinical expertise along with the bioinformatic and biostatistical analysis. Finally, M. Dadlani
M AN U
introduced the metagenomic pipeline used by CosmosID and underlined the need for identifying bacteria beyond the species level, as it was pointed last year with the Peer Bork presentation [1]. For example, Escherichia coli can be either an enterohaemorragic pathovar or a simple, harmless commensal.
TE D
The importance of contaminants in clinical metagenomics This issue was raised again in several talks and all clinical metagenomics workflows presented during ICCMg2 included negative control(s). Remaining issues are to define the appropriate negative
EP
control(s). S. Miller uses the elution buffer as negative control, as many do. Interestingly, he also uses “swipe tests” consisting in sampling the environment surrounding the sample preparation in order to
AC C
broaden the range of detection of possible contaminants outside the reagents themselves. Besides, R. Patel warned against the many contaminants brought by whole genome amplification (WGA) kits [5]. Indeed, in her findings, the microbial composition of prosthetic joint infection samples clustered according to the WGA kits, and not according to the samples themselves as it would be expected.
The bottleneck of DNA extraction and the universal pipeline One of the key issues in clinical metagenomics is to remove the host DNA. In this perspective, most speakers in ICCMg2 reported specific methods to deplete human DNA. J. O’Grady and S. Hauser showed very efficient methods that removed up to 99.99% human DNA without substantial loss of
ACCEPTED MANUSCRIPT bacterial DNA, but their protocols could not be detailed as patents were being filed. R. Patel reported that better results in human DNA removal on prosthetic joint samples were achieved using the Molysis
TM
®
®
enrichment kit (Molzym ) when compared to the NEBNext Microbiome Enrichment kit ®
(NEBNext ) [6]. Moreover, the trend towards a universal clinical metagenomic test already raised during ICCMg1 continued with ICCMg2. Hence, in order to detect RNA viruses at least, RNA needs to
RI PT
be extracted and cDNA sequenced (such as in the workflow presented by C. Chiu). During the RNA extraction, no human RNA depletion is currently used, so that the expression of the host could be analysed in parallel, as it has recently been reported in respiratory samples [7]. Indeed, provided an
SC
acceptable depth of sequencing, taking the host’s response in account together with the finding of microbes of uncertain pathogenicity could improve our interpretation of clinical metagenomic analysis. C. Chiu presented preliminary results from a machine-learning based study. He sequenced the cDNA
M AN U
(host expression) of 25 cerebrospinal fluids (CSF) with bacterial infections and 48 with viral infections as a training set and used 6 and 8 CSF with respective bacterial and viral infections as validation set. Despite a limited number of samples, the support vector machine (SVM) was able to discriminate bacterial from viral infections with 90% accuracy, suggesting the high potential of such approaches in
TE D
clinical metagenomics.
The increasing speed of clinical metagenomics
Last year, sequencing was being accelerated by using single-end, shorter reads options in Illumina ®
EP
sequencers or Nanopore sequencing. While no major breakthrough in the speed of Illumina
sequencers was reported during ICCMg2, several authors shared their experience with Nanopore ®
AC C
(Oxford Nanopore Technologies ) sequencing. C. Chiu showed examples of the application of Nanopore sequencing, including the sequencing of various clinical samples (broncho-alveolar fluids, blood, joint fluids). In less than three hours after the sample was collected, reads from the pathogen (spanning bacteria, viruses and parasites) could be detected for 11 out of 13 samples. Of note, three of these samples were analysed on-site in the Democratic Republic of Congo and in Mexico, supporting the delocalized use of clinical metagenomics offered by Nanopore sequencing. K. Schmidt showed the results of urine clinical samples (with a high load of bacterial cells) sequencing with Nanopore MinION with a four-hour turn-around time [8]. Importantly, the quality of reads increased along with the provision of new flow cells, but multiple alleles of the same antibiotic resistance gene
ACCEPTED MANUSCRIPT ®
(ARG) were still detected with the Nanopore MinION where only one gene was identified by Illumina
sequencing. J. O’Grady showed some preliminary results of the INHALE study in which 600 ®
respiratory specimens were sequenced by the Nanopore MinION. The average turn-around time (from samples to results) was eight hours (using the Rapid low input kit), which could be further reduced to five to six hours in the near future. Of note, the estimated cost per sample was 120 USD.
RI PT
Besides, like sequencing going faster and faster, bioinformatic tools are continuously improved for their performance and fastness, and it is quite hard for the user to have access to solid benchmark in order to choose the right tool for the right indication. In this perspective, the Critical Assessment of
“New” culprits identified by metagenomic studies
SC
Metagenome Interpretation (CAMI) initiative presented by A. McHardy provided valuable insights [9].
M AN U
From the sequencing of 408 joint fluid samples (195 being from aseptic failures and 213 from infections), R. Patel showed that metagenomic sequencing could identify bacteria missed by conventional methods in PJI, as previously showed in bone and joint infections by our group [10]. Particularly, she reported the finding of Mycoplasma salivarium found only by the metagenomic
A2058T in the rRNA gene.
TE D
sequencing of a PJI sample [11]. This strain was predicted to be resistant to macrolides due to a
Differentiating pathogens from harmless bacteria in clinical metagenomics shall be more complex than it was with conventional methods. P. Gyarmati showed that blood samples from healthy subjects
EP
could contain some bacterial DNA [12]. In his work, P. Gyarmati observed that patients had a higher diversity of bacterial species (as detected by shotgun metagenomic sequencing) when they were
AC C
neutropenic than when they recovered from neutropenia. C. Chiu reported cases where clinical metagenomics could identify a microbial pathogens while conventional methods failed: a case of a 58 year-old woman being contaminated after a lung transplant by the hepatitis E virus [13], a case of a couple on honeymoon returning from Maui with an infection caused by a worm, a fatal case of Saint-Louis encephalitis virus [14] and several other cases where microbes were identified in clinical metagenomics only. V. Küfner (awardee of the first ICCMg/SSM prize) reported the transmission of the human JC polyomavirus (JCPyV) from donor to recipient during kidney transplantation. The pathogenic role of JCPyV in such context is unknown and this virus is currently not part of the routine viral screening test of the donors. S.J. Pamp showed
ACCEPTED MANUSCRIPT results about the application of clinical metagenomics in infective endophtalmitis [15]. She reported from 14 patients a good concordance of culture and sequencing despite the absence of human DNA depletion. Of note, several negative controls were used. C. Rodriguez reported the use of clinical metagenomics in dermohypodermitis and necrotizing fasciitis in comparison to the 16S profiling and culture. Interestingly, metagenomic sequencing consistently found more microbes than 16S profiling.
RI PT
Still in some cases, bacteria identified in culture could not be detected by metagenomic sequencing. Eventually, R. Schlaberg reported that putative pathogens could be detected in 30% of children with
SC
pneumonia for which conventional testing returned negative [16].
New messages from ICCMg2 Storage and ethics
M AN U
During the first talk of ICCMg2, L. Farinelli (a pioneer of next-generation sequencing and co-inventor with Pascal Mayer of the DNA colonies) presented the tremendous evolution of sequencing from the ®
®
TM
first Solexa devices from the new Illumina NovaSeq
system. In this perspective, L. Farinelli raised
the issue of data storage. Clinical metagenomics requires a high number of reads in order to detect putative subdominant microbial population(s) and their antimicrobial resistance determinants.
TE D
Moreover, as clinical metagenomics is moving to universal pipeline, both DNA and cDNA should be sequenced. Consequently, the weight of the fastq files obtained from the sequencers is expected to grow bigger and bigger. Hence, should we keep the raw data and pay for the cost of storage, or did
EP
we already reach a point where re-sequencing is cheaper than storage? Will regulatory agencies require the storage of fastq files but also intermediate files (as some bioinformatic tools do not
AC C
produce the exact same results even when applied to the same material) for traceability? L. Farinelli also raised the points of ethics in sequencing. Deep sequencing of samples could bring out information that was not primarily investigated. Hence, should the patient or the doctor access the “unwanted” information or will we keep a right of not knowing it? L. Farinelli concluded his speech in stressing the importance of applying NGS for the good of humankind and not for profit.
Importance of quality Quality in clinical metagenomics was chosen to be discussed in ICCMg2 during a dedicated session, but several speakers also addressed the quality issue during their talks. P. Beurdeley detailed the
ACCEPTED MANUSCRIPT steps leading to the CE marking of the iDTECT
TM
test, in compliance with the European directive
98/79/EC. From the academic side, S. Miller presented the implementation of clinical metagenomics with respect to the Clinical Laboratory Improvement Amendments (CLIA) with a special emphasis on quality control (QC) and quality assurance (QA). S. Miller reviewed the different QC methods: reagent QC, external and internal controls, sample processing controls (SPC) and the control of
RI PT
contamination. He shared the experience of his laboratory about the identification of critical reagents that need to be validated in the laboratory prior to use (such as enzymes). As for external controls, his lab has set up a consortium of DNA virus (CMV), RNA virus (HIV), bacteria (Klebsiella pneumoniae,
SC
Streptococcus agalactiae), fungi (Aspergills niger), yeast (Cryptococcus neoformans) and a parasite (Toxoplasma gondii) as a positive control, and uses the elution buffer (of the DNA extraction kit) as a negative control. Besides, he uses two phages (the RNA MS2 phage and the DNA T1 phage) as
M AN U
internal controls for the whole process. As for identifying true microbial DNA in the sample from contamination, he uses a threshold of 10x the number of reads of the contaminant when present. Aside from the sequencing of the elution buffer, S. Miller also implemented the realisation of “swipe tests” aiming at spanning the environmental microbiome surrounding the whole clinical metagenomics process. All the microbial species found in the negative controls are stored in a dedicated database
TE D
aiming at tracking contamination over time. From the regulatory agencies point of view, K. Roth exposed the process required by the US Food and Drugs Administration (FDA) for a new test to obtain clearance. K. Roth highlighted the many opportunities offered by clinical metagenomics i.e. the
EP
possibility of identifying fastidious organisms, identify common microbes in atypical infections or uncommon microbes in common infections, and to increase the diagnostic yield in challenging clinical
AC C
syndromes such as pneumonia. Nonetheless, the validation of performances and the reporting of results creates a challenging issue for regulatory agencies. K. Roth showed the example of mass spectrometry which was recently introduced in the routine microbiology laboratories. In mass spectrometry, bacteria and yeasts are identified using a score reflecting the degree of confidence of the measure. Similarly, the identification of microbes by clinical metagenomics could be supported by a confidence score that could possibly be based on the abundance of reads (in comparison with that from negative controls), the extent of the genome coverage of reference species (e.g. the presence of reads covering 95% of the genome of Staphylococcus aureus with a 10x coverage would indicate with a high confidence that S. aureus was actually present in the sample) together with satisfactory results
ACCEPTED MANUSCRIPT for internal controls. In this perspective, S. Hauser showed some reports from direct sequencing of respiratory samples that could be used as examples for building clinical reports of clinical metagenomics. Eventually, K. Roth insisted on the clinical validation of NGS-based tests as they may identify microbes for which clinical relevance remains to be determined.
RI PT
Resistance
M. Ellington reported the work undertaken by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) subcommittee on the role of whole genome sequencing (WGS) in antibiotic
SC
susceptibility testing (AST). Indeed, assessing the link between the genotype and the phenotype at the genomic level is essential to move on to the metagenomic level (which raises other difficulties such as connecting resistance genes to their actual host). The issue of the ARG database remains,
M AN U
such as highlighted by P. Lemercier during his talk about databases. Several have been published but none has been qualified for clinical purpose. Moreover, to date none includes clinically-relevant metadata such as the antibiotics to which the ARG confers resistance to, the usual host and the location on mobile genetic elements. Eventually, such a database should be sustained and continuously updated since new ARGs are continuously described, most being alleles of known ARGs
TE D
but some being new such as the plasmid-encoded colistin-conferring resistance Mcr [17]. During their talks, O. Barraud and G. Dantas showed the potential of metagenomics to uncover the diversity of resistance genes from various environments. Such studies are crucial to understand how resistance
EP
genes circulate outside healthcare structures. Besides, newly identified resistance genes should be included in the clinical ARG database in order to increase the exhaustiveness and to be able to
AC C
identify the presence of unexpected ARG in pathogens. As of today, knowing the AST profile of bacteria drives the antibiotic regimen in that ineffective therapy increases the risk of clinical failure. On the other hand, the administration of a proper antibiotic regimen does not guarantee the clinical success, so that other bacterial or hosts determinants not directly involved in antibiotic resistance can be involved. While conventional AST indeed assesses whether a bacterium is susceptible or resistant to an antibiotic in the context of infection, clinical metagenomics could offer complementary information about not only antibiotic resistance but clinical resistance, defined as the capacity of an infection to persist despite an appropriate treatment. Possibly, some markers in the host response and bacterial virulence factors
ACCEPTED MANUSCRIPT could be identified by clinical metagenomics as associated to clinical resistance, shifting our understanding of microbiological testing. As M. Ellington mentioned during his talk: “a minimal inhibitory concentration (MIC) reflects more than gene presence / absence”. We could add that the clinical prognosis reflects more than an MIC, and this part could possibly be addressed by clinical
RI PT
metagenomics.
Perspectives
The second ICCMg confirmed that clinical metagenomics is attracting more and more attention from
SC
clinicians, microbiologists and bioinformaticians. The presentations given during ICCMg2 were more diverse than last year (with a special mention to F. Maixner who introduced paleo-clinical metagenomics) in that they covered more subjects and originated from more groups than for ICCMg1.
M AN U
More than ever, we feel that ICCMg shall accompany the clinical metagenomics wave in being the place for discussion and debate for people aiming at implementing clinical metagenomics. Accordingly, ICCMg3 will be organized next October 18-19, 2018 in Geneva.
Acknowledgements
TE D
We would like to thank the speakers who accepted to participate to the ICCMg2, the people who attended the conference and the sponsors (the American Society of Microbiology [ASM], Campus Biotech Foundation, the Federation of European Microbiology Societies [FEMS], Geneva University
EP
Hospitals [HUG], Swiss National Fund 31CO30_175439, Becton-Dickinson, Danone, Ofxord Nanopore Technologies, Roche, bioMérieux, Fasteris, Ferring, Institut Mérieux, MaaT Pharma, Nestlé
AC C
and Pathoquest) who supported the event. We are also grateful to the Campus Biotech Foundation (Ms Isabelle Bonne and Mr Benoît Dubuis) who kindly welcomed us, the Geneva University Hospitals (Ms Serena Baldelli) for the communication (program and leaflets), Julien Gregorio for the pictures taken during the conference, Mr Sylvain Morin (Concrétisateur) for the website and Kuoni Destination Management (Ms Ségolène Boudou and Mr Huw Francis) for their major contribution to the organization of this conference. Finally, we would like to give a very special thanks to Mr Enrico Zuffi and his team (Bijan Ali Hamouda and Lisa Zuffi) for their tremendous work and efforts for holding this second ICCMg.
ACCEPTED MANUSCRIPT References [1]
Ruppé E, Greub G, Schrenzel J. Messages from the first International Conference on Clinical Metagenomics (ICCMg). Microbes Infect 2017;19:223–8.
[2]
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010;464:59–65. Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, et al. An integrated catalog of reference
RI PT
[3]
genes in the human gut microbiome. Nat Biotechnol 2014;32:834–41. [4]
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human
[5]
SC
microbiome. Nature 2012;486:207–14.
Thoendel M, Jeraldo P, Greenwood-Quaintance KE, Yao J, Chia N, Hanssen AD, et al. Impact
M AN U
of contaminating DNA in whole-genome amplification kits used for metagenomic shotgun sequencing for infection diagnosis. J Clin Microbiol 2017;55:1789–801. [6]
Thoendel M, Jeraldo PR, Greenwood-Quaintance KE, Yao JZ, Chia N, Hanssen AD, et al. Comparison of microbial DNA enrichment tools for metagenomic whole genome sequencing. J Microbiol Methods 2016;127:141–5.
[7]
Langelier C, Zinter MS, Kalantar K, Yanik GA, Christenson S, O’Donovan B, et al. Metagenomic
TE D
sequencing detects respiratory pathogens in hematopoietic cellular transplant patients. Am J Respir Crit Care Med 2017. doi:10.1164/rccm.201706-1097LE. [Epub ahead of print] [8]
Schmidt K, Mwaigwisya S, Crossman LC, Doumith M, Munroe D, Pires C, et al. Identification of
EP
bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicrob Chemother 2017. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical Assessment
AC C
[9]
of Metagenome Interpretation-a benchmark of metagenomics software. Nat Methods 2017;14:1063–71.
[10] Ruppé E, Lazarevic V, Girard M, Mouton W, Ferry T, Laurent F, et al. Clinical metagenomics of bone and joint infections: a proof of concept study. Sci Rep 2017;7:7718. [11] Thoendel M, Jeraldo P, Greenwood-Quaintance KE, Chia N, Abdel MP, Steckelberg JM, et al. A possible novel prosthetic joint infection pathogen, Mycoplasma salivarium, identified by metagenomic shotgun sequencing. Clin Infect Dis 2017;65:332–5.
ACCEPTED MANUSCRIPT [12] Nikkari S, McLaughlin IJ, Bi W, Dodge DE, Relman DA. Does blood of healthy subjects contain bacterial ribosomal DNA? J Clin Microbiol 2001;39:1956–9. [13] Murkey JA, Chew KW, Carlson M, Shannon CL, Sirohi D, Sample HA, et al. Hepatitis E virusassociated meningoencephalitis in a lung transplant recipient diagnosed by clinical metagenomic sequencing. Open Forum Infect Dis 2017;4:ofx121.
RI PT
[14] Chiu CY, Coffey LL, Murkey J, Symmes K, Sample HA, Wilson MR, et al. Diagnosis of fatal human case of St. Louis encephalitis virus infection by metagenomic sequencing, California, 2016. Emerg Infect Dis 2017;23:1964–8.
SC
[15] Kirstahler P, Bjerrum SS, Friis-Moller A, Cour M la, Aarestrup FM, Westh H, et al. Genomicsbased identification of microorganisms in human ocular body fluid. BioRxiv 2017:176529. [16] Schlaberg R, Queen K, Simmon K, Tardif K, Stockmann C, Flygare S, et al. Viral pathogen
M AN U
detection by metagenomics and pan viral group PCR in children with pneumonia lacking identifiable etiology. J Infect Dis 2017;215:1407–15.
[17] Liu Y-Y, Wang Y, Walsh TR, Yi L-X, Zhang R, Spencer J, et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological
AC C
EP
TE D
and molecular biological study. Lancet Infect Dis 2016;16:161–8.
ACCEPTED MANUSCRIPT Legends Figure 1: Logo of the second International Conference on Clinical Metagenomics (ICCMg2).
Figure 2: Pictures from the conference. A: The audience and C. Rodriguez delivering his talk. B: The audience and C. Chiu delivering his talk.
C: Interactions during the poster session. D: The
by J. Schrenzel and E. Ruppé.
Figure 3: Main messages from ICCMg2
M AN U
Table 1: Speakers and titles of the talks delivered at ICCMg2.
SC
RI PT
ICCMg/SSM prize awarded to Ms Verena Küfner and the ASM prize awarded to Ms Mónika Számel
Table 2: Summary of the take-home messages and related key-points of the ICCMg2. CLIA: Clinical
AC C
EP
TE D
Laboratory Improvement Amendments. ISO: International Organization for Standardization.
Speaker Laurent Farinelli Update in sequencing Alice McHardy and bioinformatics Philippe Lemercier Pascale Beurdeley Regulatory issues raised Steve Miller by clinical metagenomics Kristian Roth Charles Chiu Justin O'Grady Christophe Rodriguez Manoj Dadlani Verena Kufner Robin Patel Clinical metagenomics Katarzina Schmidt Sébastien Hauser Sünje Johanna Pamp
Salt Lake City (United States)
Frank Maixner
Bolzano (Italy) Saint-Louis (United States)
Matthew Elligton Olivier Barraud Oluf Pedersen Peter Gyarmati Patrick Veiga
London (United Kingdom) Limoges (France) Copenhagen (Denmark) Peoria (United States) Palaiseau (France)
Title of the presentation From yesterday to tomorrow: past, present and future of sequencing Critical Assessment of Metagenome Interpretation On the importance of databases Experience of the development of iDTECT, the first NGS based CE-IVD test for pathogen detection Experience from an academic: QC in clinical metagenomics pipeline Potential approaches to validation of sequencing based assays (video-conference) Clinical metagenomics: our real-life experience Rapid metagenomic diagnosis of pneumonia Clinical metagenomics of dermohypodermitis Metagenomics for rapid pathogen detection and characterization of the microbiome in health and disease Virus transmission during kidney transplantation assessed by virome analysis of living donor and recipient Clinical metagenomics in bone and joint infections Nanopore sequencing of urine samples Clinical metagenomics of hospital-acquired pneumonia Clinical metagenomics in endophthalmitis Detection of previously missed pathogens in immunocompromised children with suspected pulmonary infections by a fully-validated metagenomics-based test Clinical metagenomics applied to human mummifed remains – Reconstruction of a 5,300-year-old Helicobacter pylori genome from the Iceman’s stomach Understanding and combatting resistome exchange across commensal, environmental, and pathogenic microbes Wrap up of the NGS/EUCAST consultation NGS for characterization of clinical class 1 integrons from hospital effluents So what? Clinical impact of microbiome studies The blood microbiome in health and disease Metagenomics: think before you speak
TE D
M AN U
SC
Gautam Dantas
EP
Microbiota
Robert Schlaberg
AC C
Antibiotic resistance
ACCEPTED MANUSCRIPT
City (Country) Geneva (Switzerland) Dusseldorf (Germany) Geneva (Switzerland) Paris (France) San Francisco (United States) Rockville (United States) San Francisco (United States) Norwich (United Kingdom) Créteil (France) Rockville (United States) Zurich (Switzerland) Rochester (United States) Norwich (United Kingdom) Grenoble (France) Lyngby (Denmark)
RI PT
Session
ACCEPTED MANUSCRIPT
Message
Key points
Push the identification of bacteria up to the strain level. Case-control studies: towards more complex design to address causality. Importance of a biological/clinical expertise along with the bioinformatic and biostatistical analysis What negative control(s) should be used? The importance of contaminants in clinical metagenomics How to substract the contaminants from the results? Consider viruses (DNA and RNA), bacteria, antibiotic resistance genes, fungi, parasites in a single pipeline. Extract DNA and RNA. Towards a universal pipeline and consequences on the nucleic acids extraction. Consider the host's gene expression. Several efficient solutions (most unpublished yet) to remove human DNA. Fast results within hours with Nanopore sequencing, yet quality still not optimal. The increasing fastness of clinical metagenomics Pathogenicity of unexpected microbes? “New” culprits identified by metagenomic studies Already actionable results when conventional methods fail to identify any causative microbe. Microbiome studies
Adapt CLIA or ISO15189 requirements to the clincal metagenomics workflow. Validation of the method: towards a confidence score (like mass spectrometry?) Are clinical parameters the best comparator to validate clinical metagenomics tests? EUCAST consultation: the WGS antibiogram not for now, but works well for some couples bacterium-antibiotic. Metagenomics allows to identify new resistance genes. Need for a database of resistance genes and associated metadata. Towards a clinical resistance with clinical metagenomics instead of an antimicrobial resistance?
RI PT
Quality
AC C
EP
TE D
M AN U
SC
Antibiotic resistance
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
A
EP AC C
C
TE D
M AN U
SC
RI PT
B
D
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT