Microbiology, Metagenomics and Bioinformatics

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

Browsing Posts tagged Biodiversity

I am very happy to announce that a first public beta version of Metaxa2 version 2.2 has been released today! This new version brings two big and a number of small improvements to the Metaxa2 software (1). The first major addition is the introduction of the Metaxa2 Database Builder, which allows the user to create custom databases for virtually any genetic barcoding region. The second addition, which is related to the first, is that the classifier has been rewritten to have a more solid mathematical foundation. I have been promising that these updates were coming “soon” for one and a half years, but finally the end-product is good enough to see some real world testing. Bear in mind though that this is still a beta version that could contain obscure bugs. Here follows a list of new features (with further elaboration on a few below):

  • The Metaxa2 Database Builder
  • Support for additional barcoding genes, virtually any genetic region can now be used for taxonomic classification in Metaxa2
  • The Metaxa2 database repository, which can be accessed through the new metaxa2_install_database tool
  • Improved classification scoring model for better clarity and sensitivity
  • A bundled COI database for athropods, showing off the capabilities of the database builder
  • Support for compressed input files (gzip, zip, bzip, dsrc)
  • Support for auto-detection of database locations
  • Added output of probable taxonomic origin for sequences with reliability scores at each rank, made possible by the updated classifier
  • Added the -x option for running only the extraction without the classification step
  • Improved memory handling for very large rRNA datasets in the classifier (millions of sequences)
  • This update also fixes a bug in the metaxa2_rf tool that could cause bias in very skewed datasets with small numbers of taxa

The new version of Metaxa2 can be downloaded here, and for those interested I will spend the rest of this post outlining the Metaxa2 Database Builder. The information below is also available in a slightly extended version in the software manual.

The major enhancement in Metaxa2 version 2.2 is the ability to use custom databases for classification. This means that the user can now make their own database for their own barcoding region of choice, or download additional databases from the Metaxa2 Database Repository. The selection of other databases is made through the “-g” option already existing in Metaxa2. As part of these changes, we have also updated the classification scoring model for better stringency and sensitivity across multiple databases and different genes. The old scoring system can still be used by specifying the –scoring_model option to “old”.

There are two different main operating modes of the Metaxa2 Database Builder, as well as a hybrid mode combining the features of the two other modes. The divergent and conserved modes work in almost completely different ways and deal with two different types of barcoding regions. The divergent mode is designed to deal with barcoding regions that exhibit fairly large variation between taxa within the same taxonomic domain. Such regions include, e.g., the eukaryotic ITS region, or the trnL gene used for plant barcoding. In the other mode – the conserved mode – a highly conserved barcoding region is expected (at least within the different taxonomic domains). Genes that fall into this category would be, e.g., the 16S SSU rRNA, and the bacterial rpoB gene. This option would most likely also be suitable for barcoding within certain groups of e.g. plants, where similarity of the barcoding regions can be expected to be high. There is also a third mode – the hybrid mode – that incorporates features of both the other. The hybrid mode is more experimental in nature, but could be useful in situations where both the other modes perform poorer than desired.

In the divergent (default) mode, the database builder will start by clustering the input sequences at 20% identity using USEARCH (2). All clusters generated from this process are then individually aligned using MAFFT (3). Those alignments are split into two regions, which are used to build two hidden Markov models for each cluster of sequences. These models will be less precise, but more sensitive than those generated in the conserved mode. In the divergent mode, the database builder will attempt to extract full-length sequences from the input data, but this may be less successful than in the conserved mode.

In the conserved mode, on the other hand, the database builder will first extract the barcoding region from the input sequences using models built from a reference sequence provided (see above) and the Metaxa2 extractor (1). It will then align all the extracted sequences using MAFFT and determine the conservation of each position in the alignment. When the criteria for degree of conservation are met, all conserved regions are extracted individually and are then re-aligned separately using MAFFT. The re-aligned sequences are used to build hidden Markov models representing the conserved regions with HMMER (4). In this mode, the classification database will only consist of the extracted full-length sequences.

In the hybrid mode, finally, the database builder will cluster the input sequences at 20% identity using USEARCH, and then proceed with the conserved mode approach on each cluster separately .

The actual taxonomic classification in Metaxa2 is done using a sequence database. It was shown in the original Metaxa2 paper that replacing the built-in database with a generic non-processed one was detrimental to performance in terms of accuracy (1). In the database builder, we have tried to incorporate some of the aspects of the manual database curation we did for the built-in database that can be automated. By default, all these filtration steps are turned off, but enabling them might drastically increase the accuracy of classifications based on the database.

To assess the accuracy of the constructed database, the Metaxa2 Database Builder allows for testing the detection ability and classification accuracy of the constructed database. This is done by sub-dividing the database sequences into subsets and rebuilding the database using a smaller (by default 90%), randomly selected, set of the sequence data (5). The remaining sequences (10% by default) are then classified using Metaxa2 with the subset database. The number of detections, and the numbers of correctly or incorrectly classified entries are recorded and averaged over a number of iterations (10 by default). This allows for obtaining a picture of the lower end of the accuracy of the database. However, since the evaluation only uses a subset of all sequences included in the full database, the performance of the full database actually constructed is likely to be slightly better. The evaluation can be turned on using the “–evaluate T” option.

Metaxa2 2.2 also introduces the database repository, from which the user can download additional databases for Metaxa2. To download new databases from the repository, the metaxa2_install_database command is used. This is a simple piece of software but requires internet access to function.

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. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
  3. Katoh K, Standley DM: MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution, 30, 772–780 (2013).
  4. Eddy SR: Accelerated profile HMM searches. PLoS Computational Biology, 7, e1002195 (2011).
  5. Richardson RT, Bengtsson-Palme J, Johnson RM: Evaluating and Optimizing the Performance of Software Commonly Used for the Taxonomic Classification of DNA Sequence Data. Molecular Ecology Resources, 17, 4, 760–769 (2017). doi: 10.1111/1755-0998.12628

Myself, Joakim Larsson and Erik Kristiansson have written a review on the environmental factors that influence development and spread of antibiotic resistance, which was published today in FEMS Microbiology Reviews. The review (1) builds on thoughts developed in the latter parts of my PhD thesis (2), and seeks to provide a synthesis knowledge gained from different subfields towards the current understanding of evolutionary and ecological processes leading to clinical appearance of resistance genes, as well as the important environmental dispersal barriers preventing spread of resistant pathogens.

We postulate that emergence of novel resistance factors and mobilization of resistance genes are likely to occur continuously in the environment. However, the great majority of such genetic events are unlikely to lead to establishment of novel resistance factors in bacterial populations, unless there is a selection pressure for maintaining them or their fitness costs are negligible. To enable measures to prevent resistance development in the environment, it is therefore critical to investigate under what conditions and to what extent environmental selection for resistance takes place. Selection for resistance is likely less important for the dissemination of resistant bacteria, but will ultimately depend on how well the species or strain in question thrives in the external environment. Metacommunity theory (3,4) suggests that dispersal ability is central to this process, and therefore opportunistic pathogens with their main habitat in the environment may play an important role in the exchange of resistance factors between humans and the environment. Understanding the dispersal barriers hindering this exchange is not only key to evaluate risks, but also to prevent resistant pathogens, as well as novel resistance genes, from reaching humans.

Towards the end of the paper, we suggest certain environments that seem to be more important from a risk management perspective. We also discuss additional problems linked to the development of antibiotic resistance, such as increased evolvability of bacterial genomes (5) and which other types of genes that may be mobilized in the future, should the development continue (1,6). In this review, we also further develop thoughts on the relative risks of re-recruiting and spreading well-known resistance factors already circulating in pathogens, versus recruitment of completely novel resistance genes from environmental bacteria (7). While the latter case is likely to be very rare, and thus almost impossible to quantify the risks for, the consequences of such (potentially one-time) events can be dire.

I personally think that this is one of the best though-through pieces I have ever written, and since it is open access and (in my biased opinion) written in a fairly accessible way, I recommend everyone to read it. It builds on the ecological theories for resistance ecology developed by, among others, Fernando Baquero and José Martinez (8-13). Over the last year, it has been stressed several times at meetings (e.g. at the EDAR conferences in August) that there is a need to develop an ecological framework for antibiotic resistance genes. I think this paper could be one of the foundational pillars on such an endeavor and look forward to see how it will fit into the growing literature on the subject!

References

  1. Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews, accepted manuscript (2017). doi: 10.1093/femsre/fux053
  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. Bengtsson J: Applied (meta)community ecology: diversity and ecosystem services at the intersection of local and regional processes. In: Verhoef HA, Morin PJ (eds.). Community Ecology: Processes, Models, and Applications. Oxford: Oxford University Press, 115–130 (2009).
  4. Leibold M, Norberg J: Biodiversity in metacommunities: Plankton as complex adaptive systems? Limnology and Oceanography, 1278–1289 (2004).
  5. Gillings MR, Stokes HW: Are humans increasing bacterial evolvability? Trends in Ecology and Evolution, 27, 346–352 (2012).
  6. Gillings MR: Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome. Frontiers in Microbiology, 4, 4 (2013).
  7. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  8. Baquero F, Alvarez-Ortega C, Martinez JL: Ecology and evolution of antibiotic resistance. Environmental Microbiology Reports, 1, 469–476 (2009).
  9. Baquero F, Tedim AP, Coque TM: Antibiotic resistance shaping multi-level population biology of bacteria. Frontiers in Microbiology, 4, 15 (2013).
  10. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  11. Hiltunen T, Virta M, Laine A-L: Antibiotic resistance in the wild: an eco-evolutionary perspective. Philosophical Transactions of the Royal Society B: Biological Sciences, 372 (2017) doi: 10.1098/rstb.2016.0039.
  12. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).
  13. Salyers AA, Amábile-Cuevas CF: Why are antibiotic resistance genes so resistant to elimination? Antimicrobial Agents and Chemotherapy, 41, 2321–2325 (1997).

Mitochondrial DNA Part B today published a mitochondrial genome announcement paper (1) in which I was involved in doing the assemblies and annotating them. The paper describes the mitogenome of Calanus glacialis, a marine planktonic copepod, which is a keystone species in the Arctic Ocean. The mitogenome is 20,674 bp long, and includes 13 protein-coding genes, 2 rRNA genes and 22 tRNA genes. While this is of course note a huge paper, we believe that this new resource will be of interest in understanding the structure and dynamics of C. glacialis populations. The main work in this paper has been carried out by Marvin Choquet at Nord University in Bodø, Norway. So hats off to him for great work, thanks Marvin! The paper can be read here.

Reference

  1. Choquet M, Alves Monteiro HJ, Bengtsson-Palme J, Hoarau G: The complete mitochondrial genome of the copepod Calanus glacialis. Mitochondrial DNA Part B, 2, 2, 506–507 (2017). doi: 10.1080/23802359.2017.1361357 [Paper link]

Yesterday, Molecular Ecology Resources put online an unedited version of a recent paper which I co-authored. This time, Rodney Richardson at Ohio State University has made a tremendous work of evaluating three taxonomic classification software – the RDP Naïve Bayesian Classifier, RTAX and UTAX – on a set of DNA barcoding regions commonly used for plants, namely the ITS2, and the matK, rbcL, trnL and trnH genes.

In the paper (1), we discuss the results, merits and limitations of the classifiers. In brief, we found that:

  • There is a considerable trade-off between accuracy and sensitivity for the classifiers tested, which indicates a need for improved sequence classification tools (2)
  • UTAX was superior with respect to error rate, but was exceedingly stringent and thus suffered from a low assignment rate
  • The RDP Naïve Bayesian Classifier displayed high sensitivity and low error at the family and order levels, but had a genus-level error rate of 9.6 percent
  • RTAX showed high sensitivity at all taxonomic ranks, but at the same time consistently produced the high error rates
  • The choice of locus has significant effects on the classification sensitivity of all tested tools
  • All classifiers showed strong relationships between database completeness, classification sensitivity and classification accuracy

We believe that the methods of comparison we have used are simple and robust, and thereby provides a methodological and conceptual foundation for future software evaluations. On a personal note, I will thoroughly enjoy working with Rodney and Reed again; I had a great time discussing the ins and outs of taxonomic classification with them! The paper can be found here.

References and notes

  1. Richardson RT, Bengtsson-Palme J, Johnson RM: Evaluating and Optimizing the Performance of Software Commonly Used for the Taxonomic Classification of DNA Sequence Data. Molecular Ecology Resources, Early view (2016). doi: 10.1111/1755-0998.12628 [Paper link]
  2. This is something that several classifiers also showed in the evaluation we did for the Metaxa2 paper (3). Interestingly enough, Metaxa2 is better at maintaining high accuracy also as sensitivity is increased.
  3. 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, 15, 6, 1403–1414 (2015). doi: 10.1111/1755-0998.12399 [Paper link]

Late yesterday, Microbiome put online our most recent work, covering the resistomes to antibiotics, biocides and metals across a vast range of environments. In the paper (1), we perform the largest characterization of resistance genes, mobile genetic elements (MGEs) and bacterial taxonomic compositions to date, covering 864 different metagenomes from humans (350), animals (145) and external environments such as soil, water, sewage, and air (369 in total). All the investigated metagenomes were sequenced to at least 10 million reads each, using Illumina technology, making the results more comparable across environments than in previous studies (2-4).

We found that the environment types had clear differences both in terms of resistance profiles and bacterial community composition. Humans and animals hosted microbial communities with limited taxonomic diversity as well as low abundance and diversity of biocide/metal resistance genes and MGEs. On the contrary, the abundance of ARGs was relatively high in humans and animals. External environments, on the other hand, showed high taxonomic diversity and high diversity of biocide/metal resistance genes and MGEs. Water, sediment and soil generally carried low relative abundance and few varieties of known ARGs, whereas wastewater and sludge were on par with the human gut. The environments with the largest relative abundance and diversity of ARGs, including genes encoding resistance to last resort antibiotics, were those subjected to industrial antibiotic pollution and air samples from a Beijing smog event.

A paper investigating this vast amount of data is of course hard to describe in a blog post, so I strongly suggest the interested reader to head over to Microbiome’s page and read the full paper (1). However, here’s a ver short summary of the findings:

  • The median relative abundance of ARGs across all environments was 0.035 copies per bacterial 16S rRNA
  • Antibiotic-polluted environments have (by far) the highest abundances of ARGs
  • Urban air samples carried high abundance and diversity of ARGs
  • Human microbiota has high abundance and diversity of known ARGs, but low taxonomic diversity compared to the external environment
  • The human and animal resistomes are dominated by tetracycline resistance genes
  • Over half of the ARGs were only detected in external environments, while 20.5 % were found in human, animal and at least one of the external environments
  • Biocide and metal resistance genes are more common in external environments than in the human microbiota
  • Human microbiota carries low abundance and richness of MGEs compared to most external environments

Importantly, less than 1.5 % of all detected ARGs were found exclusively in the human microbiome. At the same time, 57.5 % of the known ARGs were only detected in metagenomes from environmental samples, despite that the majority of the investigated ARGs were initially encountered in pathogens. Still, our analysis suggests that most of these genes are relatively rare in the human microbiota. Environmental samples generally contained a wider distribution of resistance genes to a more diverse set of antibiotics classes. For example, the relative abundance of beta-lactam resistance genes was much larger in external environments than in human and animal microbiomes. This suggests that the external environment harbours many more varieties of resistance genes than the ones currently known from the clinic. Indeed, functional metagenomics has resulted in the discovery of many novel ARGs in external environments (e.g. 5). This all fits well with an overall much higher taxonomic diversity of environmental microbial communities. In terms of consequences associated with the potential transfer of ARGs to human pathogens, we argue that unknown resistance genes are of greater concern than those already known to circulate among human-associated bacteria (6).

This study describes the potential for many external environments, including those subjected to pharmaceutical pollution, air and wastewater/sludge, to serve as hotspots for resistance development and/or transmission of ARGs. In addition, our results indicate that these environments may play important roles in the mobilization of yet unknown ARGs and their further transmission to human pathogens. To provide guidance for risk-reducing actions we – based on this study – suggest strict regulatory measures of waste discharges from pharmaceutical industries and encourage more attention to air in the transmission of antibiotic resistance (1).

References

  1. 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
  2. Durso LM, Miller DN, Wienhold BJ. Distribution and quantification of antibiotic resistant genes and bacteria across agricultural and non-agricultural metagenomes. PLoS One. 2012;7:e48325.
  3. Nesme J, Delmont TO, Monier J, Vogel TM. Large-scale metagenomic-based study of antibiotic resistance in the environment. Curr Biol. 2014;24:1096–100.
  4. Fitzpatrick D, Walsh F. Antibiotic resistance genes across a wide variety of metagenomes. FEMS Microbiol Ecol. 2016. doi:10.1093/femsec/fiv168.
  5. Allen HK, Moe LA, Rodbumrer J, Gaarder A, Handelsman J. Functional metagenomics reveals diverse β-lactamases in a remote Alaskan soil. ISME J. 2009;3:243–51.
  6. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1

MycoKeys today put a paper online which I was involved in. The paper describes the results of a workshop in May, when we added and refined annotations for fungal ITS sequences according to the MIxS-Built Environment annotation standard (1). Fungi have been associated with a range of unwanted effects in the built environment, including asthma, decay of building materials, and food spoilage. However, the state of the metadata annotation of fungal DNA sequences from the built environment is very much incomplete in public databases. The workshop aimed to ease a little part of this problem, by distributing the re-annotation of public fungal ITS sequences across 36 persons. In total, we added or changed of 45,488 data points drawing from published literature, including addition of 8,430 instances of countries of collection, 5,801 instances of building types, and 3,876 instances of surface-air contaminants. The results have been implemented in the UNITE database and shared with other online resources. I believe, that distributed initiatives like this (and the ones I have been involved in in the past (2,3)) serve a very important purpose for establishing better annotation of sequence data, an issue I have brought up also for sequences outside of barcoding genes (4). The full paper can be found here.

References

  1. Abarenkov K, Adams RI, Laszlo I, Agan A, Ambrioso E, Antonelli A, Bahram M, Bengtsson-Palme J, Bok G, Cangren P, Coimbra V, Coleine C, Gustafsson C, He J, Hofmann T, Kristiansson E, Larsson E, Larsson T, Liu Y, Martinsson S, Meyer W, Panova M, Pombubpa N, Ritter C, Ryberg M, Svantesson S, Scharn R, Svensson O, Töpel M, Untersehrer M, Visagie C, Wurzbacher C, Taylor AFS, Kõljalg U, Schriml L, Nilsson RH: Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden). MycoKeys, 16, 1–15 (2016). doi: 10.3897/mycokeys.16.10000
  2. 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
  3. 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, 67, 1, 11–19 (2014). doi: 10.1007/s13225-014-0291-8
  4. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, Early view (2016). doi: 10.1002/pmic.201600034

After a long wait (1), Science of the Total Environment has finally decided to make our paper on selection of antibiotic resistance genes in sewage treatment plants (STPs) available (2). STPs are often suggested to be “hotspots” for emergence and dissemination of antibiotic-resistant bacteria (3-6). However, we actually do not know if the selection pressures within STPs, that can be caused either by residual antibiotics or other co-selective agents, are sufficiently large to specifically promote resistance. To better understand this, we used shotgun metagenomic sequencing of samples from different steps of the treatment process (incoming water, treated water, primary sludge, recirculated sludge and digested sludge) in three Swedish STPs in the Stockholm area to characterize the frequencies of resistance genes to antibiotics, biocides and metal, as well as mobile genetic elements and taxonomic composition. In parallel, we also measured concentrations of antibiotics, biocides and metals.

We found that only the concentrations of tetracycline and ciprofloxacin in the influent water were above those that we predict to cause resistance selection (7). However, there was no consistent enrichment of resistance genes to any particular class of antibiotics in the STPs, neither for biocide and metal resistance genes. Instead, the most substantial change of the bacterial communities compared to human feces (sampled from Swedes in another study of ours (8)) occurred already in the sewage pipes, and was manifested by a strong shift from obligate to facultative anaerobes. Through the treatment process, resistance genes against antibiotics, biocides and metals were not reduced to the same extent as fecal bacteria were.

Worryingly, the OXA-48 beta-lactamase gene was consistently enriched in surplus and digested sludge. OXA-48 is still rare in Swedish clinical isolates (9), but provides resistance to carbapenems, one of our most critically important classes of antibiotics. However, taken together metagenomic sequencing did not provide clear support for any specific selection of antibiotic resistance. Rather, since stronger selective forces affect gross taxonomic composition, and thereby also resistance gene abundances, it is very hard to interpret the metagenomic data from a risk-for-selection perspective. We therefore think that comprehensive analyses of resistant vs. non-resistant strains within relevant species are warranted.

Taken together, the main take-home messages of the paper (2) are:

  • There were no apparent evidence for direct selection of resistance genes by antibiotics or co-selection by biocides or metals
  • Abiotic factors (mostly oxygen availability) strongly shape taxonomy and seems to be driving changes of resistance genes
  • Metagenomic and/or PCR-based community studies may not be sufficiently sensitive to detect selection effects, as important shifts towards resistant may occur within species and not on the community level
  • The concentrations of antibiotics, biocides and metals were overall reduced, but not removed in STPs. Incoming concentrations of antibiotics in Swedish STPs are generally low
  • Resistance genes are overall reduced through the treatment process, but far from eliminated

References and notes

  1. Okay, those who takes notes know that I have already complained once before on Science of the Total Environment’s ridiculously long production handling times. But, seriously, how can a journal’s production team return the proofs for after three days of acceptance, and then wait seven weeks before putting the final proofs online? I still wonder what is going on beyond the scenes, which is totally obscure because the production office also refuses to respond to e-mails. Not a nice publishing experience this time either.
  2. 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, in press (2016). doi: 10.1016/j.scitotenv.2016.06.228 [Paper link]
  3. Rizzo L, Manaia C, Merlin C, Schwartz T, Dagot C, Ploy MC, Michael I, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: a review. Science of the Total Environment, 447, 345–360 (2013). doi: 10.1016/j.scitotenv.2013.01.032
  4. Laht M, Karkman A, Voolaid V, Ritz C, Tenson T, Virta M, Kisand V: Abundances of Tetracycline, Sulphonamide and Beta-Lactam Antibiotic Resistance Genes in Conventional Wastewater Treatment Plants (WWTPs) with Different Waste Load. PLoS ONE, 9, e103705 (2014). doi: 10.1371/journal.pone.0103705
  5. Yang Y, Li B, Zou S, Fang HHP, Zhang T: Fate of antibiotic resistance genes in sewage treatment plant revealed by metagenomic approach. Water Research, 62, 97–106 (2014). doi: 10.1016/j.watres.2014.05.019
  6. Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015). doi: 10.1038/nrmicro3439
  7. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140–149 (2016). doi: 10.1016/j.envint.2015.10.015
  8. 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
  9. Hellman J, Aspevall O, Bengtsson B, Pringle M: SWEDRES-SVARM 2014. Consumption of antimicrobials and occurrence of antimicrobial resistance in Sweden. Public Health Agency of Sweden and National Veterinary Institute, Solna/Uppsala, Sweden. Report No.: 14027. Available from: http://www.folkhalsomyndigheten.se/publicerat-material/ (2014)

Yesterday, Ecological Informatics put our paper describing Metaxa2 Diversity Tools online (1). Metaxa2 Diversity Tools was introduced with Metaxa2 version 2.1 and consists of

  • 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 resampling rarefaction curves (2) 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

At the same time as I did this update to the web site, I also took the opportunity to update the Metaxa2 FAQ to better reflect recent updates to the Metaxa2 software.

Metaxa2 Diversity Tools
One often requested feature of Metaxa2 (3) has been the ability to make simple analyses 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-fledged community analysis package comparable to QIIME (4) or Mothur (5)). It 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 resampling rarefaction analysis (2) 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 and the recent paper (1).

The latest version of Metaxa2, including the Metaxa2 Diversity Tools, can be downloaded here.

References

  1. Bengtsson-Palme J, Thorell K, Wurzbacher C, Sjöling Å, Nilsson RH: Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2. Ecological Informatics, 33, 45–50 (2016). doi: 10.1016/j.ecoinf.2016.04.004 [Paper link]
  2. Gotelli NJ, Colwell RK: Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4, 379–391 (2000). doi:10.1046/j.1461-0248.2001.00230.x
  3. 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]
  4. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
  5. 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).

Metaxa2 has been updated again today to version 2.1.3. This update adds a few features to the Metaxa2 Diversity Tools (metaxa2_uc and metaxa2_rf). The core Metaxa2 programs remain the same as for the previous Metaxa2 versions. The new features were suggested as part of the review process of a Metaxa2-related manuscript, and we thank the anonymous reviewers for their great suggestions!

New features and bug fixes in this update:

  • Added the Chao1, iChao1 and ACE estimators in addition to the original species abundance (“Bengtsson-Palme”) model in metaxa2_rf
  • Added the Raup-Crick dissimilarity method to the metaxa2_uc tool
  • Added a warning message when data is highly skewed for metaxa2_uc
  • Improved robustness of the ‘model’ mode of metaxa2_uc for highly skewed sample groups
  • Fixed a bug causing miscalculation of Euclidean distances on binary data in metaxa2_uc

The updated version of Metaxa2 can be downloaded here.

Happy barcoding!

After a long wait (1) Sara Lundström’s paper establishing minimal selective concentrations (MSCs) for the antibiotic tetracycline in complex microbial communities (2), of which I am a co-author, has gone online. Personally, I think this paper is among the finest work I have been involved in; a lot of good science have gone into this publication. Risk assessment and management of antibiotics pollution is in great need of scientific data to underpin mitigation efforts (3). This paper describes a method to determine the minimal selective concentrations of antibiotics, and investigates different endpoints for measuring those MSCs. The method involves a testing system highly relevant for aquatic communities, in which bacteria are allowed to form biofilms in aquaria under controlled antibiotic exposure. Using the system, we find that 1 μg/L tetracycline selects for the resistance genes tetA and tetG, while 10 μg/L tetracycline is required to detect changes of phenotypic resistance. In short, the different endpoints studied (and their corresponding MSCs) were:

  • CFU counts on R2A plates with 20 μg/mL tetracycline – MSC = 10 μg/L
  • MIC range – MSC ~ 10-100 μg/L
  • PICT, leucine uptake after short-term TC challenge – MSC ~ 100 μg/L
  • Increased resistance gene abundances, metagenomics – MSC range: 0.1-10 μg/L
  • Increased resistance gene abundances, qPCR (tetA and tetG) – MSC ≤ 1 μg/L
  • Changes to taxonomic diversity – no significant changes detected
  • Changes to taxonomic community composition – MSC ~ 1-10 μg/L

This study confirms that the estimated PNECs we reported recently (4) correspond well to experimentally determined MSCs, at least for tetracycline. Importantly, the selective concentrations we report for tetracycline overlap with those that have been reported in sewage treatment plants (5). We also see that tetracycline not only selects for tetracycline resistance genes, but also resistance genes against other classes of antibiotics, including sulfonamides, beta-lactams and aminoglycosides. Finally, the approach we describe can be used for improved in risk assessment for (also other) antibiotics, and to refine the emission limits we suggested in a recent paper based on theoretical calculations (4).

References and notes

  1. Okay, seriously: how can a journal’s production team return the proofs for a paper within 24 hours of acceptance, and then wait literally five weeks before putting the final proofs online? Nothing against STOTEN, but I honestly wonder what was going on beyond the scenes here.
  2. Lundström SV, 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]
  3. Ågerstrand M, Berg C, Björlenius B, Breitholtz M, Brunstrom B, Fick J, Gunnarsson L, Larsson DGJ, Sumpter JP, Tysklind M, Rudén C: Improving environmental risk assessment of human pharmaceuticals. Environmental Science and Technology (2015). doi:10.1021/acs.est.5b00302
  4. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140-149 (2016). doi: 10.1016/j.envint.2015.10.015
  5. Michael I, Rizzo L, McArdell CS, Manaia CM, Merlin C, Schwartz T, Dagot C, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Research, 47, 957–995 (2013). doi:10.1016/j.watres.2012.11.027