Tag: DNA sequencing

I am very happy to announce that Metaxa2 version 2.1 has been released today. This new version brings a lot of important improvements to the Metaxa2 software (1), in particular by the introduction of the Metaxa2 Diversity Tools. This is the list of new features (further elaboration follows below):

• The Metaxa2 Diversity Tools:
  • metaxa2_dc – a tool for collecting several .taxonomy.txt output files into one large abundance matrix, suitable for analysis in, e.g., R
  • metaxa2_rf – generates rarefaction curves based on the .taxonomy.txt output
  • metaxa2_si – species inference based on guessing species data from the other species present in the .taxonomy.txt output file
  • metaxa2_uc – a tool for determining if the community composition of a sample is significantly different from others through resampling analysis
• Added a new detection mode for detection of multiple rRNA in the same sequence, e.g. a genome
• Added the --reference option to improve the use of Metaxa2 as a tool to sort out host rRNA sequences from a dataset
• Added the --split_pairs option causing Metaxa2 to output paired-end sequences into two separate files, which is nice for further analysis of rRNA reads
• The default setting for the --align option has been changed to ‘none‘
• Automatic detection of which BLAST package that is installed
• Fixed a bug causing the last read of paired-end FASTA input to be ignored
• Fixed an occasionally occurring BLAST+ related warning message
• Fixed a bug that could cause the classifier to crash on highly divergent BLAST matches

The new version of Metaxa2 can be downloaded here, and for those interested I will spend the rest of this post outlining the new features.

Metaxa2 Diversity Tools
One often requested feature of Metaxa2 is the ability to further make simple analysis from the data after classification. The Metaxa2 Diversity Tools included in Metaxa2 2.1 is a seed for such an effort (although not close to a full-fledge community analysis package compared to QIIME (2) or Mothur (3)). The set currently consist of four tools

The Metaxa2 Data Collector (metaxa2_dc) is the simplest of them (but probably the most requested), designed to merge the output of several *.level_X.txt files from the Metaxa2 Taxonomic Traversal Tool into one large abundance matrix, suitable for further analysis in, for example, R. The Metaxa2 Species Inference tool (metaxa2_si) can be used to further infer taxon information on, for example, the species level at a lower reliability than what would be permitted by the Metaxa2 classifier, using a complementary algorithm. The idea is that is if only a single species is present in, e.g., a family and a read is assigned to this family, but not classified to the species level, that sequence will be inferred to the same species as the other reads, given that it has more than 97% sequence identity to its best reference match. This can be useful if the user really needs species or genus classifications but many organisms in the studied species group have similar rRNA sequences, making it hard for the Metaxa2 classifier to classify sequences to the species level.

The Metaxa2 Rarefaction analysis tool (metaxa2_rf) performs a rarefaction analysis based on the output from the Metaxa2 classifier, taking into account also the unclassified portion of rRNAs. The Metaxa2 Uniqueness of Community analyzer (metaxa2_uc), finally, allows analysis of whether the community composition of two or more samples or groups is significantly different. Using resampling of the community data, the null hypothesis that the taxonomic content of two communities is drawn from the same set of taxa (given certain abundances) is tested. All these tools are further described in the manual.

The genome mode
Metaxa2 has long been said not to be useful for predicting rRNA in longer sequences, such as full genomes or chromosomes, since it has traditionally only looked for a single rRNA hit. With Metaxa2 2.1, it is now possible to use Metaxa2 on longer sequences to detect multiple rRNA occurrences. To do this, you need to change the operating mode using the new --mode option to either ‘auto‘ or ‘genome‘. The auto mode will treat sequences longer than 2500 bp as “genome” sequences and look for multiple matches in these.

The reference mode
Another feature request that has been addressed in the new Metaxa2 version is the ability to filter out certain sequences from the data set. For example, you may want to exclude all rRNA sequences that are derived from to host organism, but keep the analysis of all other rRNA reads. This is now possible by supplying a file of reference rRNA sequences to exclude in FASTA format to the --reference option.

Experimental Usearch support
Finally, we have toyed around with support for Usearch (4) instead of BLAST (5) as the search algorithm for the classification step. However, this is far from fine-tuned and it is included as an experimental feature that you may use on your own risk! We recommend that you not use it for classification of data for publication yet. However, we are interested in how this works for you, so if you like you may test to run the Usearch algorithm in parallel with your BLAST-based analysis and compare the results and send me your input on how it works. You can read more about using Usearch at the end of the Metaxa2 manual.

References

1. Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved Identification and Taxonomic Classification of Small and Large Subunit rRNA in Metagenomic Data. Molecular Ecology Resources (2015). doi: 10.1111/1755-0998.12399 [Paper link]
2. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
3. Schloss PD, Westcott SL, Ryabin T et al.: Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75, 7537–7541 (2009).
4. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
5. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 25, 3389–3402 (1997).

The paper we published in August on travelers carrying resistance genes with them in their gut microbiota has now been typeset and got proper volume and issue numbers assigned to it in Antimicrobial Agents and Chemotherapy. Take a look at it, I personally think it’s quite good-looking.

Also, if you understand Swedish, here is an interview with me broadcasted on Swedish Radio last month about this study and the consequences of it.

The new citation for the paper is:

• Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrobial Agents and Chemotherapy, 59, 10, 6551-6560 (2015). doi: 10.1128/AAC.00933-15 [Paper link]

I have had the pleasure to be chosen as a speaker for next week’s (ten days from now) Swedish Bioinformatics Workshop. My talk is entitled “Turn up the signal – wipe out the noise: Gaining insights into bacterial community functions using metagenomic data“, and will largely deal with the same questions as my talk on EDAR3 in May this year. As then, the talk will highlight the some particular pitfalls related to interpretation of data, and exemplify how flawed analysis practices can result in misleading conclusions regarding community function, and use examples from our studies of environments subjected to pharmaceutical pollution in India, the effect of travel on the human resistome, and modern municipal wastewater treatment processes.

The talk will take place on Thursday, September 24, 2015 at 16:30. The full program for the conference can be found here. And also, if you want a sneak peak of the talk, you can drop by on Friday 13.00 at Chemistry and Molecular Biology, where I will give a seminar on the same topic in the Monthly Bioinformatic Practical Meetings series.

Earlier today, my most recent paper (1) became available online, describing resistance gene patterns in the gut microbiota of Swedes before and after travel to the Indian peninsula and central Africa. In this work, we have used metagenomic sequencing of the intestinal microbiome of Swedish students returning from exchange programs to show that the abundance of antibiotic resistance genes in several classes are increased after travel. This work reiterates the findings of several papers describing uptake of resistant bacteria (2-8) or resistance genes (9-11) after travel to destinations with worse resistance situation.

Our paper is important because it:

1. Addresses the abundance of a vast range of resistance genes (more than 300).
2. Finds evidence for that the overall relative abundance of antibiotic resistance genes increased after travel, without any intake of antibiotics.
3. Shows that the sensitivity of metagenomics was, despite very deep sequencing efforts, not sufficient to detect acquisition of the low-abundant (CTX-M) resistance genes responsible for observed ESBL phenotypes.
4. Reveals a “core resistome” of resistance genes that are more or less omnipresent, and remain relatively stable regardless of travel, while changes seem to occur in the more variable part of the resistome.
5. Hints at increased abundance of Proteobacteria after travel, although this increase could not specifically be linked to resistance gene increases.
6. Uses de novo metagenomic assembly to physically link resistance genes in the same sample, giving hints of co-resistance patterns in the gut microbiome.

The paper was a collaboration with Martin Angelin, Helena Palmgren and Anders Johansson at Umeå University, and was made possible by bioinformatics support from SciLifeLab in Stockholm. I highly recommend reading it as a complement to e.g. the Forslund et al. paper (12) describing country-specific antibiotic resistance patterns in the gut microbiota.

Taken together, this study offers a broadened perspective on how the antibiotic resistance potential of the human gut microbiome changes after travel, providing an independent complement to previous studies targeting a limited number of bacterial species or antibiotic resistance genes. Understanding how resistance genes travels the globe is hugely important, since resistance in principle only need to appear in a pathogen once; improper hygiene and travel may then spread novel resistance genes across continents rapidly (13,14).

References

1. Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrob Agents Chemother Accepted manuscript posted online (2015). doi: 10.1128/AAC.00933-15 [Paper link]
2. Gaarslev K, Stenderup J: Changes during travel in the composition and antibiotic resistance pattern of the intestinal Enterobacteriaceae flora: results from a study of mecillinam prophylaxis against travellers’ diarrhoea. Curr Med Res Opin 9:384–387 (1985).
3. Paltansing S, Vlot JA, Kraakman MEM, Mesman R, Bruijning ML, Bernards AT, Visser LG, Veldkamp KE: Extended-spectrum β-lactamase-producing enterobacteriaceae among travelers from the Netherlands. Emerging Infect. Dis. 19:1206–1213 (2013).
4. Ruppé E, Armand-Lefèvre L, Estellat C, El-Mniai A, Boussadia Y, Consigny PH, Girard PM, Vittecoq D, Bouchaud O, Pialoux G, Esposito-Farèse M, Coignard B, Lucet JC, Andremont A, Matheron S: Acquisition of carbapenemase-producing Enterobacteriaceae by healthy travellers to India, France, February 2012 to March 2013. Euro Surveill. 19 (2014).
5. Kennedy K, Collignon P: Colonisation with Escherichia coli resistant to “critically important” antibiotics: a high risk for international travellers. Eur J Clin Microbiol Infect Dis 29:1501–1506 (2010).
6. Tham J, Odenholt I, Walder M, Brolund A, Ahl J, Melander E: Extended-spectrum beta-lactamase-producing Escherichia coli in patients with travellers’ diarrhoea. Scand. J. Infect. Dis. 42:275–280 (2010).
7. Östholm-Balkhed Å, Tärnberg M, Nilsson M, Nilsson LE, Hanberger H, Hällgren A, Travel Study Group of Southeast Sweden: Travel-associated faecal colonization with ESBL-producing Enterobacteriaceae: incidence and risk factors. J Antimicrob Chemother 68:2144–2153 (2013).
8. Kantele A, Lääveri T, Mero S, Vilkman K, Pakkanen SH, Ollgren J, Antikainen J, Kirveskari J: Antimicrobials increase travelers’ risk of colonization by extended-spectrum betalactamase-producing enterobacteriaceae. Clin Infect Dis 60:837–846 (2015).
9. von Wintersdorff CJH, Penders J, Stobberingh EE, Oude Lashof AML, Hoebe CJPA, Savelkoul PHM, Wolffs PFG: High rates of antimicrobial drug resistance gene acquisition after international travel, The Netherlands. Emerging Infect. Dis. 20:649–657 (2014).
10. Tängdén T, Cars O, Melhus A, Löwdin E: Foreign travel is a major risk factor for colonization with Escherichia coli producing CTX-M-type extended-spectrum beta-lactamases: a prospective study with Swedish volunteers. Antimicrob Agents Chemother 54:3564–3568 (2010).
11. Dhanji H, Patel R, Wall R, Doumith M, Patel B, Hope R, Livermore DM, Woodford N: Variation in the genetic environments of bla(CTX-M-15) in Escherichia coli from the faeces of travellers returning to the United Kingdom. J Antimicrob Chemother 66:1005–1012 (2011).
12. Forslund K, Sunagawa S, Kultima JR, Mende DR, Arumugam M, Typas A, Bork P: Country-specific antibiotic use practices impact the human gut resistome. Genome Res 23:1163–1169 (2013).
13. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nat Rev Microbiol 13:396 (2015).
14. Larsson DGJ: Antibiotics in the environment. Ups J Med Sci 119:108–112 (2014).

I will be giving a talk at the Third International symposium on the environmental dimension of antibiotic resistance (EDAR2015) next month (five weeks from now. The talk is entitled “Turn up the signal – wipe out the noise: Gaining insights into antibiotic resistance of bacterial communities using metagenomic data“, and will deal with handling of metagenomic data in antibiotic resistance gene research. The talk will highlight the some particular pitfalls related to interpretation of data, and exemplify how flawed analysis practices can result in misleading conclusions regarding antibiotic resistance risks. I will particularly address how taxonomic composition influences the frequencies of resistance genes, the importance of knowledge of the functions of the genes in the databases used, and how normalization strategies influence the results. Furthermore, we will show how the context of resistance genes can allow inference of their potential to spread to human pathogens from environmental or commensal bacteria. All these aspects will be exemplified by data from our studies of environments subjected to pharmaceutical pollution in India, the effect of travel on the human resistome, and modern municipal wastewater treatment processes.

The talk will take place on Monday, May 18, 2015 at 13:20. The full scientific program for the conference can be found here. Registration for the conference is still possible, although not for the early-bird price. I look forward to see a lot of the people who will attend the conference, and hopefully also you!

A couple of days ago, a paper I have co-authored describing an ITS sequence dataset for chimera control in fungi went online as an advance online publication in Microbes and Environments. There are several software tools available for chimera detection (e.g. Henrik Nilsson‘s fungal chimera checker (1) and UCHIME (2)), but these generally rely on the presence of a chimera-free reference dataset. Until now, there was no such dataset is for the fungal ITS region, and we in this paper (3) introduce a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database (4). This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. We estimated the dataset performance on a large set of artificial chimeras to be above 99.5%, and also used the dataset to remove nearly 1,000 chimeric fungal ITS sequences from the UNITE database. The dataset can be downloaded from the UNITE repository. Thereby, it is also possible for users to curate the dataset in the future through the UNITE interactive editing tools.

References:

1. Nilsson RH, Abarenkov K, Veldre V, Nylinder S, Wit P de, Brosché S, Alfredsson JF, Ryberg M, Kristiansson E: An open source chimera checker for the fungal ITS region. Molecular Ecology Resources, 10, 1076–1081 (2010).
2. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27, 16, 2194-2200 (2011). doi:10.1093/bioinformatics/btr381
3. Nilsson RH, Tedersoo L, Ryberg M, Kristiansson E, Hartmann M, Unterseher M, Porter TM, Bengtsson-Palme J, Walker D, de Sousa F, Gamper HA, Larsson E, Larsson K-H, Kõljalg U, Edgar R, Abarenkov K: A comprehensive, automatically updated fungal ITS sequence dataset for reference-based chimera control in environmental sequencing efforts. Microbes and Environments, Advance Online Publication (2015). doi: 10.1264/jsme2.ME14121
4. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, Bates ST, Bruns TT, Bengtsson-Palme J, Callaghan TM, Douglas B, Drenkhan T, Eberhardt U, Dueñas M, Grebenc T, Griffith GW, Hartmann M, Kirk PM, Kohout P, Larsson E, Lindahl BD, Lücking R, Martín MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG, Peintner U, Peterson M, Põldmaa K, Saag L, Saar I, Schüßler A, Senés C, Smith ME, Suija A, Taylor DE, Telleria MT, Weiß M, Larsson KH: Towards a unified paradigm for sequence-based identification of Fungi. Molecular Ecology, 22, 21, 5271–5277 (2013). doi: 10.1111/mec.12481

My colleague Henrik Nilsson has been interviewed by the ResearchGate news team about the recent effort to better annotate ITS data for plant pathogenic fungi. It’s an interesting read, and I think Henrik nicely underscores why large-scale efforts for improving and correcting sequence annotations are important. You can read the interview here, and the paper they talk about is referenced below.

Nilsson RH, Hyde KD, Pawlowska J, Ryberg M, Tedersoo L, Aas AB, Alias SA, Alves A, Anderson CL, Antonelli A, Arnold AE, Bahnmann B, Bahram M, Bengtsson-Palme J, Berlin A, Branco S, Chomnunti P, Dissanayake A, Drenkhan R, Friberg H, Frøslev TG, Halwachs B, Hartmann M, Henricot B, Jayawardena R, Jumpponen A, Kauserud H, Koskela S, Kulik T, Liimatainen K, Lindahl B, Lindner D, Liu J-K, Maharachchikumbura S, Manamgoda D, Martinsson S, Neves MA, Niskanen T, Nylinder S, Pereira OL, Pinho DB, Porter TM, Queloz V, Riit T, Sanchez-García M, de Sousa F, Stefaczyk E, Tadych M, Takamatsu S, Tian Q, Udayanga D, Unterseher M, Wang Z, Wikee S, Yan J, Larsson E, Larsson K-H, Kõljalg U, Abarenkov K: Improving ITS sequence data for identification of plant pathogenic fungi. Fungal Diversity, Volume 67, Issue 1 (2014), 11–19. doi: 10.1007/s13225-014-0291-8 [Paper link]

Our paper describing the bacterial community of a polluted lake in India has now been typeset and appears in its final form in Frontiers in Microbiology. If I may say so, I think that the paper turned out to be very goodlooking and it is indeed nice to finally see it in print. The paper describes an unprecedented diversity and abundance of antibiotic resistance genes and genes enabling transfer of DNA between bacteria. We also describe a range of potential novel plasmids from the lake. Finally, the paper briefly describes a new approach to targeted assembly of metagenomic data — TriMetAss — which can be downloaded here.

Reference:
Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648

With the publication of my latest paper last week (1), I also would like to highlight some of the software underpinning the findings a bit. To get around the problem that extremely common resistance genes could be present in multiple contexts and variants, causing assembler such as Velvet (2) to perform sub-optimally, we have written a software tool that utilizes Vmatch (3) and Trinity (4) to iteratively construct contigs from reads associated with resistance genes. This could of course be used in many other situations as well, when you want to specifically assemble a certain portion of a metagenome, but suspect that that portion might be found in multiple contexts.

TriMetAss is a Perl program, employing Vmatch and Trinity to construct multi-context contigs. TriMetAss uses extracted reads associated with, e.g., resistance genes as seeds for a Vmatch search against the complete set of read pairs, extracting reads matching with at least 49 bp (by default) to any of the seed reads. These reads are then assembled using Trinity. The resulting contigs are then used as seeds for another search using Vmatch to the complete set of reads, as above. All matches (including the previously matching read pairs) are again then used for a Trinity assembly. This iterative process is repeated until a stop criteria is met, e.g. when the total number of assembled nucleotides starts to drop rather than increase. The software can be downloaded here.

References:

1. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648
2. Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18, 821–829 (2008). doi:10.1101/gr.074492.107
3. Kurtz S: The Vmatch large scale sequence analysis software (2010). http://vmatch.de/
4. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al.: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29, 644–652 (2011). doi:10.1038/nbt.1883

The first work in which I have employed metagenomics to investigate antibiotic resistance has been accepted in Frontiers in Microbiology, and is (at the time of writing) available as a provisional PDF. In the paper (1), which is co-authored by Fredrik Boulund, Jerker Fick, Erik Kristiansson and Joakim Larsson, we have used shotgun metagenomic sequencing of an Indian lake polluted by dumping of waste from pharmaceutical production. We used this data to describe the diversity of antibiotic resistance genes and the genetic context of those, to try to predict their genetic transferability. We found resistance genes against essentially every major class of antibiotics, as well as large abundances of genes responsible for mobilization of genetic material. Resistance genes were estimated to be 7000 times more abundant in the polluted lake than in a Swedish lake included for comparison, where only eight resistance genes were found. The abundances of resistance genes have previously only been matched by river sediment subject to pollution from pharmaceutical production (2). In addition, we describe twenty-six known and twenty-one putative novel plasmids from the Indian lake metagenome, indicating that there is a large potential for horizontal gene transfer through conjugation. Based on the wide range and high abundance of known resistance factors detected, we believe that it is plausible that novel resistance genes are also present in the lake. We conclude that environments polluted with waste from antibiotic manufacturing could be important reservoirs for mobile antibiotic resistance genes. This work further highlights previous findings that pharmaceutical production settings could provide sufficient selection pressure from antibiotics (3) to drive the development of multi-resistant bacteria (4,5), resistance which may ultimately end up in pathogenic species (6,7). The paper can be read in its entirety here.

References:

1. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, Volume 5, Issue 648 (2014). doi: 10.3389/fmicb.2014.00648
2. Kristiansson E, Fick J, Janzon A, Grabic R, Rutgersson C, Weijdegård B, Söderström H, Larsson DGJ: Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS ONE, Volume 6, e17038 (2011). doi:10.1371/journal.pone.0017038.
3. Larsson DGJ, de Pedro C, Paxeus N: Effluent from drug manufactures contains extremely high levels of pharmaceuticals. J Hazard Mater, Volume 148, 751–755 (2007). doi:10.1016/j.jhazmat.2007.07.008
4. Marathe NP, Regina VR, Walujkar SA, Charan SS, Moore ERB, Larsson DGJ, Shouche YS: A Treatment Plant Receiving Waste Water from Multiple Bulk Drug Manufacturers Is a Reservoir for Highly Multi-Drug Resistant Integron-Bearing Bacteria. PLoS ONE, Volume 8, e77310 (2013). doi:10.1371/journal.pone.0077310
5. Johnning A, Moore ERB, Svensson-Stadler L, Shouche YS, Larsson DGJ, Kristiansson E: Acquired genetic mechanisms of a multiresistant bacterium isolated from a treatment plant receiving wastewater from antibiotic production. Appl Environ Microbiol, Volume 79, 7256–7263 (2013). doi:10.1128/AEM.02141-13
6. Pruden A, Larsson DGJ, Amézquita A, Collignon P, Brandt KK, Graham DW, Lazorchak JM, Suzuki S, Silley P, Snape JR., et al.: Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environ Health Perspect, Volume 121, 878–885 (2013). doi:10.1289/ehp.1206446
7. Finley RL, Collignon P, Larsson DGJ, McEwen SA, Li X-Z, Gaze WH, Reid-Smith R, Timinouni M, Graham DW, Topp E: The scourge of antibiotic resistance: the important role of the environment. Clin Infect Dis, Volume 57, 704–710 (2013). doi:10.1093/cid/cit355