Elizabethkingia anophelis

DATA: raw Illumina reads (CDC) under SRP072035

Assemblies & analyseshttps://github.com/katholt/elizabethkingia


Left – core SNP tree, created from assembled genomes using Parsnp. Right – core SNP tree, created by mapping all outbreak genomes to our 9-contig assembly for SRR3240412, using our RedDog pipeline. Details below; all assemblies and mapping outputs are here.

March 19, 2016: I saw on twitter today that there was an outbreak of a weird bacteria I’ve never heard of before (Elizabethkingia anophelis) in Wisconsin, which had infected >50 people and killed almost 20.

The Wisconsin Health Services department has posted some information here (click the “For Health Professionals” tab to get info on the bacteria and antibiotic resistance).

CDC has deposited Illumina reads from 18 outbreak strains into SRA under project SRP072035 so I pulled the data and had a look. I managed to download the readsets in a few minutes (using bionode-ncbi) but it took a really long time to unpack these into fastq files using sra-toolkit.

As I have no idea about this species, I thought I’d start by looking for antibiotic resistance and plasmids in the first 6 read sets using our SRST2 software, while waiting for the rest of the reads to unpack… this ran quickly and showed me the same results for all 6 strains: GOB-10 (1 SNP from the closest allele in the ARG database) and B-2 (3 SNPs), at depths of 35-65x. For example:

SRR3240397 ARGannot.r1 GOB-1_Bla GOB-10_821 100.0 47.59 1snp 0.115 873 0.043 188 821 no;yes;GOB-10;Bla;AY647247;1-873;873
SRR3240397 ARGannot.r1 B-1_Bla B-2_1160 100.0 59.891 3snp 0.4 750 0.03 314 1160 no;no;B-2;Bla;AF189300;1-750;750

Because the matches were not identical, I pulled the consensus sequences (based on read mapping) using –report_all_consensus option in SRST2:

>314__B-1_Bla__B-2__1160 no;no;B-2;Bla;AF189300;1-750;750
>188__GOB-1_Bla__GOB-10__821 no;yes;GOB-10;Bla;AY647247;1-873;873

I had a quick look at the accessions for these genes (they are hiding in the fasta headers above) and found that they are carbapenemase genes reported from Elizabethkingia meningosepticum (previously called Chryseobacterium meningosepticum), reported in these papers: Bellais 2000, Antimicrob. Agents Chemother and Yum 2010, J Microbiol. These genes confer resistance to carbapenems like meropenem and imipenem, which probably contributes to these bacteria causing hospital-acquired infections as they will be selected for by carbapenem exposure.

SRST2 didn’t find any other acquired antibiotic resistance genes (from the ARG-Annot database) or known plasmid replicons (at least those in the PlasmidFinder database), which is consistent with the Wisconsin health services reports that these strains are susceptible to lots of readily accessible drugs including fluoroquinolones, rifampin and trimethoprim/sulfamethoxazole.

Running a quick NCBI BLAST search of the carbapenemase gene sequences shows that these new sequences, which are from outbreak strains identified definitively as Elizabethkingia anophelis by CDC, are closest to sequences annotated in NCBI as originating from Elizabethkingia meningosepticum. (The trees below are just straight out NCBI BLAST, obtained by clicking “Distance tree of results” and then downloading the newick tree files to view in FigTree.)

Screen Shot 2016-03-19 at 4.21.25 pm

The outbreak strain’s GOB-10 gene had 1 synonymous SNP compared to the reference sequence, while the B-2 gene had 1 synonymous and 2 non-synonymous SNPs (affecting codons 31 & 34, which is outside the beta-lactamase domain).

I am guessing that species assignations are pretty tricky for this genus, as few labs will have access to definitive tests to discern them, so we shouldn’t read much into this. However if it is true that the outbreak strains are Elizabethkingia anophelis and the close-matching genes in NCBI did come from Elizabethkingia meningosepticum, this would suggest that there is horizontal gene transfer between these species.

Note 1: while writing this, the fastqs finished extracting and I ran SRST2 and found the same antibiotic resistance gene results on all 18. I’m now running some SPAdes assemblies which I’ll post here later, to save others the trouble…

Note 2: the assemblies (SPAdes fasta and fastg; plus Prokka annotated in GenBank format), and various analyses including trees created using Parsnp (from assemblies) and our RedDog pipeline (mapping of reads to reference genome strain NUHP1 =CP007547) are here in github: https://github.com/katholt/elizabethkingia

The assemblies are a bit variable, but mostly ~3.9 Mbp (the reference is 4,369,828) but the best one was for SRR3240413 – 32 contigs with 3,911,053 bp total. Viewing the SPAdes assembly graph in our Bandage program shows that 3,910,660 bp are in a single linked graph, which corresponds to the chromosome. (The other little bits do not look like plasmids, just leftover bits of sequence and probably adapters, that SPAdes spit out in teeny bits of a few hundred bp each.)



The genomes look pretty similar at first glance, but interestingly 4 of them share a deletion of ~80 genes. That’s a great little epidemiological marker for the investigations.

Screen Shot 2016-03-19 at 7.24.21 pm

This was detected by our mapping pipeline RedDog, which I used to map the reads to reference genome NUHP1 CP007547 (this may not be the best reference, I just picked one randomly). The assemblies confirm it: genes BD94_0888 to BD94_0962, and the end of BD94_0963, are missing in these 4 strains (although reads do map to BD94_0948, because this is present in a second copy elsewhere in the genome).

Here’s that tree with a bit more detail (red = # core SNPs). The tree was made using FastTree, with NUHP1 reference genome as an outgroup (the outbreak strains are >40,000 SNPs away from this reference).

Screen Shot 2016-03-19 at 8.26.50 pm


David Edwards has been playing with Hybrid StriDe + SPAdes assembly recently, and tried this with SRR3240412. The SPAdes assembly (here) was 3,913,666 bp in 41 contigs. The hybrid assembly (here) is 3,917,367 bp in 9 contigs. This is what the graph looks like… I’m showing it coloured by BLAST matches to the NUPH1 reference genome so you can see what the likely path through the graph is (rainbow, red -> purple, indicates matches from start -> end coordinates of the reference genome).


March 22: Hybrid assemblies for all 18 outbreak genomes (9-15 contigs each; ranging 3,830,044 – 3,912,928 bp) are now in GitHub (thanks to David Edwards for this).

David re-ran the RedDog mapping pipeline using this genome assembly as the reference, and got a very similar tree (files in GitHub):


And here is a core genome SNP tree (made from genome assemblies using Harvest), which shows the outbreak strains are a novel lineage of E. anophelis, compared to currently available data (tree file in GitHub):


Note: Sylvain Brisse has shown the same thing using his core genome MLST scheme (see BioRxiv preprint posted March 19). I have used his nomenclature here (lineage A, lineage B). Note that Lineage B strains were originally identified as E. meningoseptica, but belong to E. anophelis. I have also included here the genome of E.meningoseptica NCTC10588, which was sequenced as part of the Sanger/PHE/PacBio type strain project, in this tree… it clusters within lineage A and is clearly E. anophelis.

UPDATE: This tweet-fest led to a collaborative project between myself, Sylvain Brisse (Institut Pasteur) and CDC, which was eventually published in Nature Communications a year after this blog post: “Evolutionary dynamics and genomic features of the Elizabethkingia anophelis 2015 to 2016 Wisconsin outbreak strain



Clarifier: Bacterial populations and communities

[This was originally posted by Kat on her BacPathGenomics blog, April 2011]

Two areas where next-gen sequencing is making a big impact in the bacterial world are the analysis of ‘bacterial populations’ and ‘bacterial communities’. While these might sound similar, they are actually very different.

In common parlance we sometimes use ‘population’ and ‘community’ somewhat interchangeably in talking about groups of humans. We might say that in Melbourne, coffee drinking is common in the local population, or that it is common in the local community. What we mean is that it’s common among people living in Melbourne.

In biology, the term ‘population’ has a specific meaning – a group of individuals of the same species (i.e. able to interbreed; but note this concept is complex in bacteria), defined by time and space. So we can talk about the currrent human population of the Earth, or the human population of Melbourne 20 years ago. Note that this is intimately tied up with the concept of species, as separation into two distinct populations is a key step towards diverging into different species. On the other hand, ‘community’ refers more generally to the group of organisms inhabiting a particular ecological niche, which could include any number of species. So for example we could talk about the population of karri trees (a species of eucalyptus found in the south west of Western Australia), or the community of plants inhabiting the karri forrest.

Bacterial populations

When we talk about bacterial populations, what we mean is investigating the population structure of a particular bacterial species/subtype… in theory this aims to understand the population in its entirety, but in practice usually involves studying lots of individual members of the population and making inferences about the population as a whole. We can attempt to understand populations at different levels of localisation…. e.g. we can study a highly localised population, like the population of Salmonella Typhi inhabiting the gall bladder of a typhoid carrier; or more expansive populations of Salmonella Typhi circulating in a city, a country or around the globe.

Sequencing has been a great tool for understanding bacterial populations, by allowing lots of individual members of a population (i.e. individual bacterial isolates or colonies) to be compared at the sequence level. Sequence data is ideal for this, as the differences between individuals are often tiny  (i.e. there is very little variation) since they belong to a single population, and DNA sequence data allows us to detect single nucleotide changes (ie provides high resolution). Also, since we have well-developed models of sequence evolution (ie how nucleotide changes accumulate), sequence data can be interpreted using phylogenetic analysis. This really kicked off a decade ago with multi-locus sequence typing (MLST; see wikipedia entry(!) or Maiden et al, 1998 for more info) and is now expanding rapidly with the advent of sequencing platforms that allow whole genomes of hundreds of isolates to be sequenced (e.g. 96 bacterial isolates can be readily sequenced in a single run of the Illumina HiSeq, using multiplexing).

This kind of analysis can be used in public health microbiology and infectious disease epidemiology to trace outbreaks or transmission (sometimes called molecular epidemiology or genomic epidemiology). It can also be used to study the evolution of drug resistance or pathogenesis/virulence in bacterial populations (microevolution, since it is occurring within populations), or the impact of a novel vaccine or drug on a given bacterial population, all of which can be useful for designing and monitoring public health interventions or making treatment recommendations.

A great recent example is the study by Nick Croucher (an immensely talented PhD student) from the Sanger Institute, and numerous collaborators, who compared the genomes of 240 Streptococcus pneumoniae isolates of the PMEN1 subtype, collected from all over the world since 1984. By comparing the genomes of these isolates, they found evidence of frequent homologous recombination with other S. pneumoniae, including exchange of genes encoding the capsule targeted by vaccination and acquisition of drug resistance genes. Assuming the sequenced isolates are reasonably representative of the global population of S. pneumoniae PMEN1, this indicates that the PMEN1 population is not isolated from the rest of the S. pneumoniae population but that there is constant gene exchange within and between S. pneumoniae groups, allowing the bacteria to escape the effects of human interventions including vaccine-induced immunity and exposure to antimicrobial drugs. We already ‘knew’ this could happen in bacterial pathogen populations, but this study provides direct evidence of it occurring in response to a specific vaccine and specific drugs used for treatment. See pubmed entry, unfortunately you need access to Science magazine to read the article.

Bacterial communities

On the other hand, when we talk about bacterial communities, what we mean is investigating the communities of bacteria present in a given sample. This is akin to walking through the forest and taking note of each plant you see, and the analysis methods borrow heavily from ecology. Studies of bacterial communities are being done in just about every kind of sample in which you would expect to find bacteria – from environmental samples (e.g. underwater caves; windscreen splatter) to human body sites (faeces or the gut; skin; nasal passages; read more at the Human Microbiome Project site).

The analysis usually focuses on determining which bacterial taxa (e.g. a genus, species or subgroup) were present in each sample, and their relative abundance. These can be compared across samples to identify taxa that are only present in certain kinds of environments, or whose presence is associated with another property of the sample (e.g. presence in the nose may be associated with development of otitis media). Communities can be examined more holistically to identify broad differences in the bacterial community structures associated with different samples.

Sequencing has dramatically improved the ease with which bacterial communities can be studied, via sequencing of DNA extracted from a given sample (e.g. a soil sample; a fecal sample). Two approaches are possible – sequence the raw DNA extract or amplify a conserved bacterial gene (using PCR) and sequence that. The first is true ‘metagenomics’, as you are sequencing all of the genomes present in the original sample, but this takes a lot sequencing effort and you may not need or want to know every single gene present in the sample. At the moment, Illumina platforms are most appropriate for this application as they have the highest throughput, however their short read lengths make assembly and analysis difficult. The second way, which usually targets the conserved 16S ribosomal RNA gene (‘16S sequencing’), is a more tractable way of determining what species/subgroups of bacteria are present in the sample and estimating their relative abundance. Multiplexing can be achieved by incorporating sample-specific barcodes into the amplicons during PCR, allowing hundreds of samples to be analysed in a single sequencing run.