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.
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.
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:
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).
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:
References and notes
I am happy to announce that our Viewpoint article on strategies for improving sequence databases has now been published in the journal Proteomics. The paper (1) defines some central problems hampering genomic, proteomic and metagenomic analyses and suggests five strategies to improve the situation:
The paper is not long, so I encourage you to read it in its entirety. We believe that spreading this knowledge and pushing solutions to problems related to poor annotation metadata is vastly important in this era of big data. Although we specifically address protein-coding genes in this paper, the same logic also applies to other types of biological sequences. In this way the paper is related to my previous work with Henrik Nilsson on improving annotation data for taxonomic barcoding genes (2-4). This paper was one of the main end-results of the GoBiG network, and the backstory on the paper follows below the references…
In June 2013, the Gothenburg Bioinformatics Group for junior scientists (GoBiG) arranged a workshop with two themes: “Parallelized quantification of genes in large metagenomic datasets” and “Assigning functional predictions to NGS data”. The following discussion on how to database quality influenced results and what could be done to improve the situation was rather intense, and several good ideas were thrown around. I took notes from the meeting, and in the evening I put them down during a warm summer night at the balcony. In fact, the notes were good enough to be an early embryo for a manuscript. So I sent it to some of the most active GoBiG members (Kaisa Thorell and Fredrik Boulund), who were positive regarding the idea to turn it into a manuscript. I wrote it together more properly and we decided that everyone who contributed with ideas at the meeting would be invited to become co-authors. We submitted the manuscript in early 2014, only to see it (rather brutally) rejected. At that point most of us were sucked up in their own projects, so nothing happened to this manuscript for over a year. Then we decided to give it another go, updated the manuscript heavily and changed a few parts to better reflect the current database situation (at this point, e.g., UniProt had already started implementing some of our suggested ideas). Still, some of the proposed strategies were more radical in 2013 than they would be now, more than three years later. We asked the Proteomics editors if they would be interested in the manuscript, and they turned out to be very positive. Indeed, the entire experience with the editors at Proteomics has been very pleasant. I am very thankful to the GoBiG team for this time, and to the editors at Proteomics who saw the value of this manuscript.
Late last year, we introduced FARAO – the Flexible All-Round Annotation Organizer – a software tool that allows visualization of annotated features on contigs. Today, the Applications Note describing the software was published as an advance access paper in Bioinformatics (1). As I have described before, storing and visualizing annotation and coverage information in FARAO has a number of advantages. FARAO is able to:
I have previously used FARAO to produce annotation figures in our paper on a polluted Indian lake (2), as well as in a paper on sewage treatment plants (which is in press and should be coming out any day now). We hope that the tool will find many more uses in other projects in the future!
So, on Thursday (May 26th) I will defend my thesis, titled “Antibiotic resistance in the environment: a contribution from metagenomic studies”. I will not dwell into this by writing a novel text, but will instead shamelessly reproduce the press release, which should give a reasonable overview of what I have been doing:
More and more people are infected with antibiotic resistant bacteria. But how do bacteria become resistant? A doctoral thesis from the Centre for Antibiotic Resistance Research at University of Gothenburg has investigated the role of the environment in the development of antibiotic resistance.
“An important question we asked was how low concentrations of antibiotics that can favour the growth of resistant bacteria in the environment”, says Johan Bengtsson-Palme, author of the thesis.
“Based on our analyses, we propose emission limits for 111 antibiotics that should not be exceeded in order to avoid that environmental bacteria become more resistant.”
A starting point to regulate antibiotic pollution
A recent report, commissioned by the British Prime Minister David Cameron, proposes that the emission limits suggested in Johan’s thesis should be used as a starting point to regulate antibiotic pollution from, for example, pharmaceutical production – globally.
“Many people are surprised that such regulations are not already in place, but today it is actually not a crime to discharge wastewater contaminated with large amounts of antibiotics, not even in Europe”, says Johan Bengtsson-Palme.
In one of the studies in the thesis, the researchers show that resistance genes against a vast range of antibiotics are enriched in an Indian lake polluted by dumping of wastewater from pharmaceutical production.
“It’s scary. Not only do the bacteria carry a multitude of resistance genes. They are also unusually well adapted to share those genes with other bacteria. If a disease-causing bacterium ends up in the lake, it may quickly pick up the genes it needs to become resistant. Since the lake is located close to residential areas, such spread of resistant bacteria to humans is not hard to imagine”, says Johan Bengtsson-Palme.
Spreading by travelers
The thesis also shows that resistant bacteria spread in the intestines of travelers who have visited India or Central Africa, even if the travelers themselves have not become sick.
“That resistant bacteria spread so quickly across the planet highlights that we must adopt a global perspective on the resistance problem”, says Johan Bengtsson-Palme. “Furthermore, it is not enough to reduce the use of antibiotics in healthcare. We must also reduce the use of antibiotics for animals, and try to limit the releases of antibiotics into the environment to try to get control over the growing antibiotic resistance problem before it is too late”.
The thesis Antibiotic resistance in the environment: a contribution from metagenomic studies will be defended on a dissertation on May 26th.
Today marks the five year anniversary for the Metaxa software’s initial release. Much has happened to the software since; Metaxa started off as an rRNA extraction utility for metagenomic data (1), including coarse classification to organism/organelle type. Since it has gained full-scale taxonomic classification ability better or on par with other software packages (2), much greater speed, support for the LSU gene, gained a range of related software tools (3), and spurred development of other tools such as ITSx (4). I have also been involved in no less than four peer-reviewed publications directly related to the software (1-3,5).
But it does not end here; these five years were just the beginning. We are – in different constellations – working on further enhancements to Metaxa2, including support for more genes, an updated classification database, and better customization options. I am very much still devoted to keep Metaxa2 alive and relevant as a tool for taxonomic analysis of metagenomes, applicable whenever accuracy is a key parameter. Thanks for being part of the community for these five years!
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
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.