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.
As the 8th Next Generation Sequencing Congress in London is drawing to a close as I write this, I have a few reflections that might warrant sharing. The first thing that has been apparent this year compared to the two previous times I have visited the event (in 2012 and 2013) is that there was very little talk about where Illumina sequencing is heading next. Instead the discussion was about the applications of Illumina sequencing in the clinical setting; so apparently this is now so mainstream that we only expect slow progress towards longer reads. Apart from that, Illumina is a completed, mature technology. Instead, the flashlight is now pointing entirely towards long-read sequencing (PacBio, NanoPore) as the next big thing. However, the excitement around these technologies has also sort of faded compared to in 2013 when they were soon-to-arrive. Indeed, it seems like there’s not much to be excited about in the sequencing field at the moment, or at least Oxford Global (who are hosting the conference) has failed to get these technologies here.
What also strikes me is the vast amounts of talk about RNAseq of cancer cells. The scope of this event has narrowed dramatically in the past three years. Which makes me substantially less interested in returning next year. If there is not much to be excited about, and the focus is only on cancer sequencing – despite the human microbiota being a very hot topic at the moment – what is the reason for non-cancer researchers to come to the event? There will need to be a stark shift towards another direction of this event if the arrangers want it to remain a broad NGS event. Otherwise, they may just as well go all in and rename the event the Next Generation Sequencing of Cancer Congress. But I hope they choose to widen the scope again; conferences discussing technology as a foundation for a variety of applications are important meeting points and spawning grounds for novel ideas.
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:
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
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).
I just want to highlight that the paper on strategies to improve database accuracy and usability we recently published in Proteomics (1) has been included in their most recent issue, which is a special issue focusing on Data Quality Issues in Proteomics. I highly recommend reading our paper (of course) and many of the other in the special issue. Happy reading!
On another note, I will be giving a talk next Wednesday (October 5th) on a seminar day on next generation sequencing in clinical microbiology, titled “Antibiotic resistance in the clinic and the environment – There and back again“. You are very welcome to the lecture hall at floor 3 in our building at Guldhedsgatan 10A here in Gothenburg if you are interested! (Bear in mind though that it all starts at 8.15 in the morning.)
Finally, it seems that I am going to the Next Generation Sequencing Congress in London this year, which will be very fun! Hope to see some of you dealing with sequencing there!
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.
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!