Microbiology, Metagenomics and Bioinformatics

Johan Bengtsson-Palme, University of Gothenburg | Wisconsin Institute for Discovery

Browsing Posts tagged Metagenomics

Yesterday, BMC Microbiology published a paper which I have co-authored with Joakim Forsell and his colleagues in at Umeå University. The paper (1) investigates the prevalence and subtype composition of Blastocystis – a eukaryotic microbe commonly present in the human intestine – among the 35 Swedish university students that we investigated for antibiotic resistance before and after travel to the Indian peninsula or Central Africa using shotgun metagenomics, and published in 2015 (2). In this paper, we used the same metagenomic data, but to assess the impact of travel on Blastocystis carriage and to understand the associations between Blastocystis and the bacterial gut microbiota. We found that 46% of the students carried Blastocystis before travel and 43% after. The two most commonly identified Blastocystis subtypes were ST3 and ST4, accounting for 20 of the 31 samples positive for Blastocystis. Interestingly, we detected no mixed subtype carriage in any individual, and all the ten individuals with a typable subtype before and after travel maintained their initial subtype.

Furthermore, we found that the composition of the gut bacterial community was not significantly altered between Blastocystis-carriers and non-carriers. Curiously, Blastocystis was accompanied with higher abundances of the bacterial genera Sporolactobacillus and Candidatus Carsonella. As perviously observed (3), Blastocystis carriage was positively associated with higher bacterial genus richness, and negatively correlated to the Bacteroides-driven enterotype. We, however, took this observation further, and could show that these associations were both largely driven by ST4 – a subtype commonly described in Europe – while the globally prevalent ST3 did not show such significant relationships.

The persistence of Blastocystis subtypes before and after travel indicates that long-term carriage of Blastocystis is common. The associations between Blastocystis and the bacterial microbiota found in this study could imply a link between Blastocystis and a healthy microbiota, as well as with diets high in vegetables. However, we cannot answer whether the associations between Blastocystis and the microbiota are resulting from the presence of Blastocystis per se, or are a prerequisite for colonization with Blastocystis, which are interesting opportunities for follow-up studies.

I think this type of data reuse for completely different questions is highly useful, and I am very happy that Joakim Forsell and his colleagues contacted me to hear if it was possible to do a Blastocystis screen of this data. The full paper can be read here.

References

  1. Forsell J, Bengtsson-Palme J, Angelin M, Johansson A, Evengård B, Granlund M: The relation between Blastocystis and the intestinal microbiota in Swedish travellers. BMC Microbiology, 17, 231 (2017). doi: 10.1186/s12866-017-1139-7 [Paper link]
  2. 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]
  3. Andersen LO, Bonde I, Nielsen HB, Stensvold CR: A retrospective metagenomics approach to studying Blastocystis. FEMS Microbiology Ecology, 91, fiv072 (2015). doi: 10.1093/femsec/fiv072 [Paper link]

Last summer, I was approached by Muniyandi Nagarajan to write a book chapter for a book on metagenomics. The book was published earlier this month, and is now available online (1). I have to admit that I have not yet read the entire book, but my own chapter deals with selecting the right tools for metagenomic analysis, and discusses different strategies to perform taxonomic classification, functional analysis, metagenomic assembly, and statistical comparisons between metagenomes (2). The chapter also considers the pros and cons of automated computational “pipelines” for analysis of metagenomic data. While I do not point to a specific set of software that obviously perform better in all situations, I do highlight some analysis strategies that clearly should be avoided. The chapter also suggests a few among the set of robust and well-functioning software tools that, in my opinion, should be used for metagenomic analyses. To some degree, this paper overlaps with the review paper we wrote on using metagenomics to analyze antibiotic resistance genes in various environments, published earlier this year (3), but the discussion in the book chapter is far more general. I imagine that the book chapter could be used, for example, in teaching metagenomics to students in bioinformatics (that’s at least a use I envision myself). Finally, apart from my own chapter, I can also highly recommend the chapter by Boulund et al. on statistical considerations for metagenomic data analysis (4). The book is available to buy from here, and the chapter can be read here.

References

  1. Nagarajan M (Ed.) Metagenomics: Perspectives, Methods, and Applications. ISBN: 9780081022689. Academic Press, Elsevier, USA (2018). doi: 10.1016/B978-0-08-102268-9 [Link]
  2. Bengtsson-Palme J: Strategies for Taxonomic and Functional Annotation of Metagenomes. In: Nagarajan M (Ed.) Metagenomics: Perspectives, Methods, and Applications, 55–79. Academic Press, Elsevier, USA (2018). doi: 10.1016/B978-0-08-102268-9.00003-3 [Link]
  3. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, 72, 2690–2703 (2017). doi: 10.1093/jac/dkx199 [Paper link]
  4. Boulund F, Pereira MB, Jonsson V, Kristiansson E: Computational and Statistical Considerations in the Analysis of Metagenomic Data. In: Nagarajan M (Ed.) Metagenomics: Perspectives, Methods, and Applications, 81–102. Academic Press,, Elsevier, USA (2018). doi: 10.1016/B978-0-08-102268-9.00004-5 [Link]

Today, Microbiome put online a paper lead-authored by my colleague Fanny Berglund – one of Erik Kristiansson‘s brilliant PhD students – in which we identify 76 novel metallo-ß-lactamases (1). This feat was made possible because of a new computational method designed by Fanny, which uses a hidden Markov model based on known B1 metallo-ß-lactamases. We analyzed over 10,000 bacterial genomes and plasmids and over 5 terabases of metagenomic data and could thereby predict 76 novel genes. These genes clustered into 59 new families of metallo-β-lactamases (given a 70% identity threshold). We also verified the functionality of 21 of these genes experimentally, and found that 18 were able to hydrolyze imipenem when inserted into Escherichia coli. Two of the novel genes contained atypical zinc-binding motifs in their active sites. Finally, we show that the B1 metallo-β-lactamases can be divided into five major groups based on their phylogenetic origin. It seems that nearly all of the previously characterized mobile B1 β-lactamases we identify in this study were likely to have originated from chromosomal genes present in species within the Proteobacteria, particularly Shewanella spp.

This study more than doubles the number of known B1 metallo-β-lactamases. As with the study by Boulund et al. (2) which we published last month on computational discovery of novel fluoroquinolone resistance genes (which used a very similar approach but on a completely different type of genes), this study also supports the hypothesis that environmental bacterial communities act as sources of uncharacterized antibiotic resistance genes (3-7). Fanny have done a fantastic job on this paper, and I highly recommend reading it in its entirety (it’s open access so you have virtually no excuse not to). It can be found here.

References

  1. Berglund F, Marathe NP, Österlund T, Bengtsson-Palme J, Kotsakis S, Flach C-F, Larsson DGJ, Kristiansson E: Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data. Microbiome, 5, 134 (2017). doi: 10.1186/s40168-017-0353-8
  2. Boulund F, Berglund F, Flach C-F, Bengtsson-Palme J, Marathe NP, Larsson DGJ, Kristiansson E: Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics, 18, 682 (2017). doi: 10.1186/s12864-017-4064-0
  3. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  4. Allen HK, Donato J, Wang HH et al.: Call of the wild: antibiotic resistance genes in natural environments. Nature Reviews Microbiology, 8, 251–259 (2010).
  5. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  6. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).
  7. Finley RL, Collignon P, Larsson DGJ et al.: The scourge of antibiotic resistance: the important role of the environment. Clinical Infectious Diseases, 57, 704–710 (2013).

BMC Genomics today published a paper first-authored by my long-time colleague Fredrik Boulund, which describes a computational screen of genomes and metagenomes for novel qnr fluoroquinolone resistance genes (1). The study makes use of Fredrik’s well-designed and updated qnr-prediction pipeline, but in contrast to his previous publication based on the pipeline from 2012 (2), we here study a 20-fold larger dataset of almost 13 terabases of sequence data. Based on this data, the pipeline predicted 611 putative qnr genes, including all previously described plasmid-mediated qnr gene families. 20 of the predicted genes were previously undescribed, and of these nine were selected for experimental validation. Six of those tested genes improved the survivability under ciprofloxacin exposure when expressed in Escherichia coli. The study shows that qnr genes are almost ubiquitous in environmental microbial communities. This study also lends further credibility to the hypothesis that environmental bacterial communities can act as sources of previously uncharacterized antibiotic resistance genes (3-7). The study can be read in its entirety here.

References

  1. Boulund F, Berglund F, Flach C-F, Bengtsson-Palme J, Marathe NP, Larsson DGJ, Kristiansson E: Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics, 18, 682 (2017). doi: 10.1186/s12864-017-4064-0
  2. Boulund F, Johnning A, Pereira MB, Larsson DGJ, Kristiansson E: A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences. BMC Genomics, 13, 695 (2012). doi: 10.1186/1471-2164-13-695
  3. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  4. Allen HK, Donato J, Wang HH et al.: Call of the wild: antibiotic resistance genes in natural environments. Nature Reviews Microbiology, 8, 251–259 (2010).
  5. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  6. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).
  7. Finley RL, Collignon P, Larsson DGJ et al.: The scourge of antibiotic resistance: the important role of the environment. Clinical Infectious Diseases, 57, 704–710 (2013).

I have just returned from a week of vacation in Sicily (almost without internet access), so I am a tad late to this news, but earlier this week Infection and Immunity published our paper on the Helicobacter pylori transcriptome in gastric infection (and early stages of carcinogenesis), and how that relates to the transcriptionally active microbiota in the stomach environment (1). This paper has been long in the making (an earlier version of it was included in Kaisa Thorell’s PhD thesis (2)), but some late additional analyses did substantially strengthen our confidence in the suggestions we got from the original data.

In the paper (1) we use metatranscriptomic RNAseq to investigate the composition of the viable microbial community, and at the same time study H. pylori gene expression in stomach biopsies. The biopsies were sampled from individuals with different degrees of H. pylori infection and/or pre-malignant tissue changes. We found that H. pylori completely dominates the microbiota in infected individuals, but (somewhat surprisingly) also in the majority of individuals classified as H. pylori uninfected using traditional methods. This confirms previous reports that have detected minute quantities of H. pylori also in presumably uninfected individuals (3-6), and raises the question of how large part of the human population (if any) that is truly not infected/colonized by H. pylori. The abundance of H. pylori was correlated with the abundance of Campylobacter, Deinococcus, and Sulfurospirillum. It is unclear, however, if these genera only share the same habitat preferences as Helicobacter, or if they are specifically promoted by the presence of H. pylori (or tissue changes induced by it). We also found that genes involved in pH regulation and nickel transport were highly expressed in H. pylori, regardless of disease stage. As far as we know, this study is the first to use metatranscriptomics to study the viable microbiota of the human stomach, and we think that this is a promising approach for future studies on pathogen-microbiota interactions. The paper (in unedited format) can be read here.

References

  1. Thorell K, Bengtsson-Palme J, Liu OH, Gonzales RVP, Nookaew I, Rabeneck L, Paszat L, Graham DY, Nielsen J, Lundin SB, Sjöling Å: In vivo analysis of the viable microbiota and Helicobacter pylori transcriptome in gastric infection and early stages of carcinogenesis. Infection and Immunity, accepted manuscript (2017). doi: 10.1128/IAI.00031-17 [Paper link]
  2. Thorell K: Multi-level characterization of host and pathogen in Helicobacter pylori-associated gastric carcinogenesis. Doctoral thesis, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg (2014). [Link]
  3. Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, Francois F, Perez-Perez G, Blaser MJ, Reman DA: Molecular analysis of the bacterial microbiota in the human stomach. PNAS, 103:732-737 (2006).
  4. Dicksved J, Lindberg M, Rosenquist M, Enroth H, Jansson JK, Engstrand L: Molecular characterization of the stomach microbiota in patients with gastric cancer and in controls. Journal of Medical Microbiology, 58:509-516 (2009).
  5. Maldonado-Contreras A, Goldfarb KC, Godoy-Vitorino F, Karaoz U, Contreras M, Blaser MJ, Brodie EL, Dominguez-Bello MG: Structure of the human gastric bacterial community in relation to Helicobacter pylori status. ISME Journal, 5:574-579 (2011).
  6. Li TH, Qin Y, Sham PC, Lau KS, Chu KM, Leung WK: Alterations in Gastric Microbiota After H. Pylori Eradication and in Different Histological Stages of Gastric Carcinogenesis. Scientific Reports, 7:44935 (2017).

Today, a review paper which I wrote together with Joakim Larsson and Erik Kristiansson was published in Journal of Antimicrobial Chemotherapy (1). We have for a long time used metagenomic DNA sequencing to study antibiotic resistance in different environments (2-6), including in the human microbiota (7). Generally, our ultimate purpose has been to assess the risks to human health associated with resistance genes in the environment. However, a multitude of methods exist for metagenomic data analysis, and over the years we have learned that not all methods are suitable for the investigation of resistance genes for this purpose. In our review paper, we describe and discuss current methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. We also provide an overview of important considerations related to the analysis of resistance genes, and end by recommending some of the currently used tools, databases and methods that are best equipped to inform research and clinical practice related to antibiotic resistance (see the figure from the paper below). We hope that the paper will be useful to researchers and clinicians interested in using metagenomic sequencing to better understand the resistance genes present in environmental and human-associated microbial communities.

References

  1. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, advance access (2017). doi: 10.1093/jac/dkx199 [Paper link]
  2. 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 [Paper link]
  3. Lundström S, Östman M, Bengtsson-Palme J, Rutgersson C, Thoudal M, Sircar T, Blanck H, Eriksson KM, Tysklind M, Flach C-F, Larsson DGJ: Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Science of the Total Environment, 553, 587–595 (2016). doi: 10.1016/j.scitotenv.2016.02.103 [Paper link]
  4. Bengtsson-Palme J, Hammarén R, Pal C, Östman M, Björlenius B, Flach C-F, Kristiansson E, Fick J, Tysklind M, Larsson DGJ: Elucidating selection processes for antibiotic resistance in sewage treatment plants using metagenomics. Science of the Total Environment, 572, 697–712 (2016). doi: 10.1016/j.scitotenv.2016.06.228 [Paper link]
  5. Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ: The structure and diversity of human, animal and environmental resistomes. Microbiome, 4, 54 (2016). doi: 10.1186/s40168-016-0199-5 [Paper link]
  6. Flach C-F, Pal C, Svensson CJ, Kristiansson E, Östman M, Bengtsson-Palme J, Tysklind M, Larsson DGJ: Does antifouling paint select for antibiotic resistance? Science of the Total Environment, 590–591, 461–468 (2017). doi: 10.1016/j.scitotenv.2017.01.213 [Paper link]
  7. 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]

In March, I attended a workshop on the role of NGS technologies in the coordinated action plan against antimicrobial resistance, organised by JRC in Italy. I was, together with 14 other experts, invited to discuss where and how sequencing can be used to investigate and manage antibiotic resistance. The report from the workshop has just recently been published, and is available here. There will be follow-up activities on this workshop, which I also hope that I will be able to participate in, since this is an important and very interesting pet topic of mine.

Reference

  • Angers A, Petrillo P, Patak, A, Querci M, Van den Eede G: The Role and Implementation of Next-Generation Sequencing Technologies in the Coordinated Action Plan against Antimicrobial Resistance. JRC Conference and Workshop Report, EUR 28619 (2017). doi: 10.2760/745099 [Link]
  • Sorry for the late notice, but if you have half an hour to spare later today I will discuss our findings on resistance genes in Beijing air on a webinar organised by Healthcare Without Harm on “The (un)recognised pathways of AMR: Air pollution and food“. Tune in a few minutes before 16.00 CEST!

    After the usual (1,2) long wait between acceptance and publication, Science of the Total Environment today put a paper online in which I have played a role in the bioinformatic analysis. In the paper, we investigate whether antifouling paint containing copper and zinc could co-select for antibiotic resistance, using microbiological methods and metagenomic sequencing (3).

    In this work, we have studied marine microbial biofilms allowed to grow on surfaces painted with antifouling paint submerged in sea water. Such antifouling paints often contain metals that potentially could co-select for antibiotic resistance (4). Using microbiological culturing, we found that the heavy-metal based paint co-selected for bacteria resistant to tetracycline. However, the paint did not enrich neither the total abundance of known mobile antibiotic resistance genes nor the abundance of tetracycline resistance genes in the biofilm communities. Rather, the communities from the painted surfaces were enriched for bacteria with genetic profiles suggesting increased capacity for extrusion of antibiotics via RND efflux systems. In addition, these communities were also enriched for genes involved in mobilization of DNA, such as ISCR transposases and integrases. Finally, the biofilm communities from painted surfaces displayed lower taxonomic diversity and were at the same time enriched for Gammaproteobacteria. The paper builds on our previous work in which we identify certain co-occurences between genes conferring metal and antibiotic resistance (4). However, the findings of this paper do not lend support for that mobile resistance genes are co-selected for by copper and zinc in the marine environment – rather the increase in antibiotic resistance seem to be due to taxonomic changes and cross-resistance mechanisms. The entire paper can be read here.

    References

    1. Bengtsson-Palme J: Published paper: Community MSCs for tetracycline. http://microbiology.se/2016/03/22/published-paper-community-mscs-for-tetracycline/
    2. Bengtsson-Palme J: Published paper: Antibiotic resistance in sewage treatment plants . http://microbiology.se/2016/08/17/published-paper-antibiotic-resistance-in-sewage-treatment-plants/
    3. Flach C-F, Pal C, Svensson CJ, Kristiansson E, Östman M, Bengtsson-Palme J, Tysklind M, Larsson DGJ: Does antifouling paint select for antibiotic resistance? Science of the Total Environment, in press (2017). doi: 10.1016/j.scitotenv.2017.01.213 [Paper link]
    4. Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential. BMC Genomics, 16, 964 (2015). doi: 10.1186/s12864-015-2153-5 [Paper link]

    I am happy to announce that the opinion/review piece I wrote for Current Opinion in Food Science has been published. The paper (1) extends on some of my thoughts on how high-throughput sequencing and metagenomics can aid in risk assessment of antibiotic resistant bacteria that I outlined in my PhD thesis (2), but specifically focuses on the food supply chain and its role in resistance dissemination and selection.

    In the paper, I argue for that the food supply chain is a special type of setting in the resistance puzzle, as it not only serves as a connection between environmental habitats for bacteria and humans, but also sometimes presents a substantial selection for resistance, due to use of antibiotics in agri- and aquaculture. International food standards are clear that both selection and dissemination of foodborne resistance should be considered in the risk analysis of food production (3). However, the current main use of DNA sequencing in food safety is whole genome sequencing to delineate which specific strains that are involved in foodborne disease outbreaks, including the resistance factors they may carry (4,5). Further, I argue that while shotgun metagenomics could be used to screen samples for a large number of genes involved in resistance and virulence in the food supply chain, it would at present be very costly and therefore of doubtful benefit to employ in routine screening programs. Still, metagenomics can contribute knowledge that can be used in quantitative risk assessment of antibiotic resistance in the food supply chain.

    The entire paper can be read here.

    References

    1. Bengtsson-Palme J: Antibiotic resistance in the food supply chain: Where can sequencing and metagenomics aid risk assessment? Current Opinion in Food Science, in press (2017). doi: 10.1016/j.cofs.2017.01.010 [Paper link]
    2. Bengtsson-Palme J: Antibiotic resistance in the environment: a contribution from metagenomic studies. Doctoral thesis (medicine), Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 2016. [Link]
    3. Codex Alimentarius Commission: Guidelines for risk analysis of foodborne antimicrobial resistance. Food and Agriculture Organization of the United Nations & World Health Organization2011. [Link]
    4. Franz E, Gras LM, Dallman T: Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens. Current Opinion in Food Science, 8, 74-79 (2016). doi: 10.1016/j.cofs.2016.04.004
    5. Stasiewicz MJ, Bakker den HC, Wiedmann M: Genomics tools in microbial food safety. Current Opinion in Food Science, 4, 105-110 (2015). doi: 10.1016/j.cofs.2015.06.002