SNP analysis and plasmid copy number among 5 outbreak E. coli

Since the Illumina (MiSeq) reads from 5 HPA genomes were recently released, I thought it would be interesting to compare these to the ‘complete’ assembly of TY2482 from BGI and look for substitution mutations and read depths for plasmids vs chromosome.

SNP analysis

I decided to try using Nesoni, the python-based analysis pipeline written by Paul Harrison & Torsten Seeman from the Victorian Bioinformatics Consortium here in Melbourne. It uses shrimp2 to map reads, and samtools and to generate and process sorted alignments and call variants. It has a nice feature where, with a single command (nesoni nway) you can generate a table showing an n-way comparison of consensus allele calls in a set of genomes, at each of the loci called as a variant in any genome.

The complete resulting output is here. It reports not only the consensus call, but the evidence behind the call, so it’s simple to see whether you believe it or not. The result is 220 calls, of which I might believe 9 (pink in figure below, and Excel file here).

I mapped MiSeq reads from 4 of the 5 HPA genomes (shrimp2 didn’t like the fastq file for sample 280, need to sort this out) and the HiSeq reads from BGI’s TY2482, to the complete reference assembly for TY2482. For 194 of the 220 variants called, the TY2482 read mapping resulted in a variant call compared to the TY2482 reference, which means that the variant is unlikely to be real. This could happen for a variety of reasons relating to the mapping & variant calling process, and I was just using the default settings in Nesoni so some tweaking might remove these. In any case, I will ignore these variants for now because I don’t believe they are real (but you can see the full table here).

This leaves 24 variant calls, where the allele detected in one or more of the 4 HPA genomes is different from the TY2482 assembly+reads from BGI, shown in the table below. This includes 9 in the chromosome (highlighted in pink), with 2 SNPs called in all 4 genomes; 1 SNP called in sample 283 only, 3 SNPs called in sample 540 and 3 SNPs called in sample 541.

SNPs identified in HPA strains compared to the complete BGI assembly for TY2482

In pTY1, which is the IncI plasmid bearing the beta-lactamase CTX-M-15 gene, the variants detected (yellow above) were all within the shufflon proteins…this region is able to ‘shuffle’ via inversions between homologous sequences, and these variant calls will most likely represent shuffling in the region rather than point mutation. In fact, the alignment suggests that there are multiple versions of these sequences in each DNA sample, suggesting that the shufflon was active and generating a mixed population of plasmids in the HPA data (but not the BGI strain TY2482, which had homozygous calls in this region). This might be worthy of some further investigation by someone who understands shufflons a lot better than I do!

Finally, there were a few variant calls in the tiny plasmid pTY3, clustered within its rep gene. These calls are heterozygous (see table) in all four HPA strains, suggesting that the mapping is picking up two different versions of the rep gene, which could be due to homology with other replication proteins in plasmids pTY1 and pTY2.

Plasmid copy number

The massive variation in read depth for plasmid sequences compared to the chromosome reminded me it might be interesting to try to infer the average copy number for each plasmid based on read depth. To do this, I used the depth plots output by nesoni (which gives the mean read depth per base in the reference sequence, based on read mapping). I calculated the mean read depth across each reference sequence (ie the completed BGI TY2482 assembly, chromosome + 3 plasmids) from this, and then calculated the ratio of read depths for plasmid:chromosome. Assuming each bacterial cell has ~1 copy of the chromosome (i.e. ignoring cells caught in the act of replication when there will be >1), this should give an approximation of the mean copy number of each plasmid per cell. We know some plasmids are maintained quite stably at 1 per cell, while others can exist at high copy number. This is the result:

Mean read depth and mean plasmid copy number for outbreak strains

So for the TY2482 data, it looks as though the IncI1 resistance plasmid (pTY1) and the aggregative adhesion plasmid (pTY2) are maintained at ~1 per cell, while the mini plasmid (which contains little more than a plasmid replication gene) is present at ~9 per cell. This is pretty much in line with expectation.

Interestingly, the HPA strains appear to have much higher copy numbers, around 20 per cell for pTY1 and pTY2 and hundreds of copies of pTY3. The numbers are pretty consistent across the HPA strains, but are remarkably higher than in TY2482.

I don’t have a good explanation for this apparent difference…. it could be an artefact in the sequencing (MiSeq likes plasmid DNA???) or in the mapping (not sure how this could be, especially since the mean depth plots produced by nesoni exclude regions that map to multiple locations in the reference genome).This could be examined by looking at results from different mapping programs, or analysis of reads from different platforms (Ion Torrent for TY2482 & LB226692, 454 reads for the HPA & C2L genomes).

If it is a real difference, I wonder if it could be differences in growth rate or culture conditions in the two labs. Or a mutation in the chromosome that affects the normal control of plasmid replication? Could having lots of copies of the aggregative adhesion plasmid enhance virulence or transmission of the bug?

Four new outbreak isolates sequenced

Just a quick post to say that, in addition to the 454 scaffold for H112180280, the UK’s Health Protection Agency has now posted Illumina MiSeq data (reads + assembly) for H112180280 and a further 4 isolates: http://www.hpa-bioinformatics.org.uk/lgp/genomes

So enters the 4th sequencing platform…

 

Eurosurveillance editorial on E. coli outbreak credits crowdsourcing

This editorial in Eurosurveillance gives a nice overview of the microbiological side of the German E. coli outbreak investigation, including applauding the public data release & analysis efforts:

The data sets from these sequencing initiatives were instantly released for public access, resulting in data analysis among bioinformaticians and other researchers around the world. Results from these preliminary analyses have been rapidly communicated via blogs, Twitter and private web pages, outside the standard peer-reviewed scientific publication route. These initiatives have confirmed the microbiological characterisation of the outbreak strain made in the public health laboratories by targeted genotyping and phenotyping of facultative E. coli virulence genes. Most importantly, among compared E. coli genome sequences, the genome of the 2011 outbreak strain clustered closest to an EAggEC strain isolated in 2002, with the addition of stx2 and antibiotic resistance genes.

The details of findings to date are outlined in this article in the same issue, including details of Shiga toxin-producing enteroaggregative E. coli O104:H4 from an outbreak in Georgia in 2009 (main difference seems to be that it was ESBL negative, unlike the current strain which has acquired an IncI plasmid carrying the ESBL gene blaCTX-M). They also discuss a rapid PCR test for the outbreak strain direct from food samples, involving enrichment+incubation (18-24h) followed by PCR for stx2 gene from extracted DNA, followed by PCR for O104 and then confirmation from pure cultures.

There are now reports of E. coli O104 from a stream in Germany, located downstream from a sewage plant… although this is more likely to be caused by the outbreak than a cause of it, it highlights that even highly industrialized countries need to be vigilant with sanitation and hygiene to prevent the spread of dangerous human pathogens [sourced from ProMed].

Enteroaggregative E. coli on LigerCat

Reading Jon Eisen’s blog this week I was rather taken with this post about LigerCat. LigerCat is an online tool that searches pubmed for whatever you ask it to, and displays a cloud of the MeSH terms (keywords attached to articles) associated with the pubmed results. It also shows a neat bar chart of article counts by year.

Since I’ve just been introduced to enteroaggregative E. coli thanks to the German E. coli outbreak, I thought I’d search for “Enteroaggregative E. coli”… this is the result.

I think this shows quite nicely that (at least according to the literature) this organism is defined by adhesion, normally associated with diarrhea in children and babies and commonly tested for by PCR.

According to this it was first described as enteroaggregative E. coli in 1989 and has been the subject of some attention, but not a lot, ever since (~15 articles per year):

The picture is quite different for “Shiga toxin“, most associated with E. coli O157 and hemolytic-uremic syndrome, with first mention in 1942 and a mass of interest since the 1980s, now with >200 papers each year:

BGI assembly of German E. coli outbreak strain

BGI has released a ‘complete’ assembly of TY2482. Here is what it looks like, with the 4 other available outbreak genomes mapped against it (4 inner rings) plus available references.

BGI assembly (TY2482, reference) vs 4 other outbreak genome assemblies (from inner: purple=GOS1, pink=GOS2, green=HPA strain, blue=LB226692). Yellow = reference sequence... for chromosome, Ec55989 and red=VT2 phage; for IncI plasmid, pEC_Bactec; for mini plasmid, pO26-S1; for EAEC plasmid, red/yellow/aqua = 55989p, pO86A1, pAA, orange=agg operon.

 

Chromosome:

BGI assembly (TY2482, reference) vs 4 other outbreak genome assemblies (from inner: purple=GOS1, pink=GOS2, green=HPA strain, blue=LB226692). Yellow = Ec55989, red=VT2 phage.

 

EAEC plasmid, novel version and carrying agg operon (aggregative adhesion fimbriae class I; AAF/I):

BGI assembly (TY2482, reference) vs 4 other outbreak genome assemblies (from inner: purple=GOS1, pink=GOS2, green=HPA strain, blue=LB226692). Red = 55989p, yellow = pO86A1, aqua = pAA, orange=agg operon.

 

IncI plasmid, carrying extended spectrum beta-lactamase blaCTX-M-15:

BGI assembly (TY2482, reference) vs 4 other outbreak genome assemblies (from inner: purple=GOS1, pink=GOS2, green=HPA strain, blue=LB226692). Yellow = pEC_Bactec.

 

Mini plasmid, replication regions only:

Mini plasmid ("selfish plasmid", carrying only rep genes). Yellow = pO26-S1.

Genome comparisons for 4 available outbreak genomes

Two new genomes were released today (well today my time, yesterday European time!) by the Göttingen Genomics Lab. They say:

We just released the 454 data of another two isolates from the German E. coli outbreak. You can find it on our website:
http://www.g2l.bio.uni-goettingen.de/
The link to the ftp server is ftp://134.76.70.117/
User name and password are ‘EAHEC_GOS’.

We just released the 454 assembly data of another two isolates (GOS1
and GOS2) from the German E. coli O104:H4 outbreak.
The shotgun libraries were sequenced on a GS FLX using Titanium
chemistry. 1.5 medium lanes of a Titanium picotiter plate was used for
each strain. Reads were assembled de novo using the Roche Newbler
assembly software 2.3.
We’ll submit the read data in the NCBI SRA as well.

There is also a new assembly from BGI which I’ve not looked at yet, go here to the github wiki to find out more.

I downloaded the first genome assembly (GOS1) but the second kept timing out, maybe they are a very popular download today! So here is the latest comparison of four available genomes from the outbreak, with their closest available reference sequences. This just illustrates what we knew from earlier analyses, and confirms that at least one of the newest genomes is pretty much the same as all the other outbreak genomes.

Comparison of 4 available assemblies (note there is a 5th but I couldn't get it to download!) For chromosome, phage and IncI plasmid, the reference sequences from NCBI are used. For the aggregative adhesion plasmid, it is so different to published references sequences that I have used the HPA scaffold as the reference, and mapped all others (including available EAEC reference plasmids and the agg operon) to this.

Genomics Workshop – Melbourne

I’m helping to organise a genomics workshop in Melbourne, designed to accommodate student and postdoc researchers with a background in either biology or computational/technical sciences. It will run twice, on June 30 & July 19. It’s filling up fast so if you’re local and interested, register quick.

 

VLSCI Workshop. Life Sciences meet Technical Sciences: Strings attached to ‘omics.
Program overview Download workshop details Register
. Date: A one-day workshop held on both Thursday 30 June and Tuesday 19 July 2011 Location: Parkville Maximum 16 participants per workshop; attendance is free. Participants will be researchers at graduate and post-graduate level. This workshop is designed for biologists, computer scientists, mathematicians and engineers: to learn about ‘omics research to establish some lines of communication across the disciplines to look for synergies through active learning and application exercises. Cutting edge scientific research demands interdisciplinary collaboration. This is especially obvious in the Life Sciences, where computational techniques are revolutionising the way many problems are tackled. However, establishing links between the Life Sciences and the Technical Sciences remains difficult because of communication barriers. This workshop aims to break down those barriers.  Participants will exchange ideas, enjoy some ‘cultural exchanges’ and look for potential for joint research projects. With a focus on ‘omics research, ie. sequencing technology and analysis, we plan to create a group of like-minded researchers who are interested in interdisciplinary exchange and possible research synergies. The morning program covers sequencing technology and analysis and learning about some of the challenges in faced by Life Scientists involved in ‘omics research. The afternoon will shift focus on to learning about how Computer Scientists approach programming. Participants will work in teams of two and in small groups to foster discussion and communication. Prerequisite: Participants need to have a laptop with a wireless connection. A console needs to be installed (Windows: Putty; Mac: Terminal). Max. participants: 16 (8 from Life Sciences and 8 from Computer Science). If a workshop is oversubscribed, participants are chosen at random and alternate dates for a follow on workshop will be offered to meet the demand.

Point mutations (SNPs) in outbreak strain relative to Ec55989

This post is really a reply to PatrikD’s comments on my last post about the SNP-based phylogeny of E. coli, showing the close relationship between the outbreak EAEC O104:H4 strain and the finished genome sequence of Ec55989 (well, chromosome). I can’t post much in a comment reply so it requires a new post.

PatrikD said:

Since I’m looking for SNPs that agree between the three assemblies, I’m not too worried about remaining sequencing errors. Given how closely these strains were sampled, I’m not interested in any minor differences between them, but more in the difference between 55989 and the three outbreak strains.

Now, I think the best way to do that at this point would be to use the SNP calls from HiSeq reads of TY2482. I get 1781 high quality SNP calls compared to Ec55989, see table here (using bwa & samtools to map reads and call SNPs). Most of these are in prophage regions (annotated in this artemis-readable file and flagged in the SNP table in the ‘mobile’ column); excluding these there are 564 probable SNPs (SNP table, artemis-readable file).

This is their distribution (dark blue = phage regions and IS elements; red = SNP calls overlapping with these mobile regions; green = 564 SNP calls not in these regions; inner ring=GC content):

Green = SNPs in the outbreak genome (TY2482, Hiseq) compared to Ec55989

Of the 1230 SNPs in mobile regions (i.e. subject to horizontal transfer or homologous recombination with typically horizontally transferred genes), there are 713 non-synonymous mutations (i.e. changes the encoded amino acid) and 424 synonymous changes (i.e. silient mutations, not affecting the encoded amino acid sequence)…while it is dodgy to do dN/dS on this kind of intraspecies data, it would be ~0.5, consistent with purifying selection.

For the 564 SNPs in non-mobile regions of the genome (i.e. not subject to horizontal transfer or homologous recombination with typically transferred genes), 284 are non-synonymous and 145 synonymous (dN/dS~0.6). Again it’s tempting to call this purifying selection, as it looks like mutations affecting protein function are being selectively removed, relative to those silent mutations. Also, 136 SNPs (24%) were in intergenic regions. Since only 13% of the chromosome is non-coding, this suggests that mutations in non-coding regions are better tolerated than those in coding sequences, again consistent with purifying selection against mutations affecting protein function.

There are 4,762 genes annotated in Ec55989, and only 564 SNPs…so the majority of genes don’t have any point mutations (although I haven’t looked at insertion/deletions yet, only subsitution mutations where one DNA base is exhanged for another). But do any genes have a high number of changes?

The answer is yes, but these mostly look like recombination events:

ibrA, an immunoglobulin-binding regulator, has 20 SNPs, mainly in the C-terminal domain. It’s quite likely this is a recombination event rather than diversifying selection, but deserves a closer look. A neighbouring gene, EC55989_3315 (conserved hypothetical) also has 18 SNPs, so recombination is a likely culprit.

The Ag43 gene, EC55989_3357, has 17 SNP calls, however we know from the assemblies that the outbreak strain has acquired divergent copies of Ag43 genes [see posts on analysis of integrated sequence, and biofilm genes], so these SNP calls probably reflect the differences between two distinct copies of Ag43 genes rather than point mutations acquired in the EC55989_3357 gene itself.

There are 6 SNPs in EC55989_4669, an Iha adhesin receptor. This could be real, or could be a divergent copy acquired by the outbreak strain.

Hypothetical proteins EC55989_3283, EC55989_3284, EC55989_3293, EC55989_3355, EC55989_4845, EC55989_4890, EC55989_4891, EC55989_4896, EC55989_4897 each have 3-4 SNPs. These are probably phage remnants or similar to phage genes, but some followup analysis could confirm this. There are 7 SNPs in shiA, a fragment of a gene similar to a shigella SHI-2 pathogenicity island gene. EC55989_4671 is an IS element that slipped through my filter, and contains 5 SNP calls.

SNP-base phylogeny confirms similarity of E. coli outbreak to EAEC Ec55989

Thanks to Konrad Paszkiewicz from University of Exeter for this SNP-based analysis of the 3 E. coli outbreak genomes.

He used MUMmer to compare each complete E. coli genome available in NCBI to the Ec55989 chromosome, and identify single nucleotide polymorphisms (SNPs, i.e. substitution mutations, where one DNA base is swapped for another). He ignored SNPs in regions that are repeated in the Ec55989 genome, since we can’t distinguish whether these represent true substitution mutations in homologous sequences (relevant phylogenetic signal), or variation between copies in the same genome (noise). The results are available here.

He did the same comparison for the three outbreak genome assemblies, including the strain TY2482 from BGI (NCBI: AFOB00000000; Illumina HiSeq+Ion Torrent), strain LB226692 from Life Sciences (NCBI: AFOG00000000; Ion Torrent), and strain H112180280 from the UK Health Protection agency (454 mate pair). In addition he did the same comparison for the first MIRA assembly of TY2482 (Ion Torrent) and I analysed SNPs in Ty2482 using the Illumina HiSeq reads released by BGI (using bwa and samtools).

Konrad then used the annotation of Ec55989 to identify the coding change cause by each SNP – i.e. whether it is in a gene, and whether the DNA base change results in a change in the encoded protein sequence (non-synonymous SNP) or not (synonymous SNP). See resulting table here.

I used this annotation to remove SNP calls in genes annotated with the keywords ‘phage’, ‘transposase’, ‘insertion sequence’ or ‘IS’ in an effort to remove obviously horizontally transferred DNA…since this is not only subject to divergence by mutation (the phylogenetic signal we are looking for) but also to recombination. The result is just over 400,000 SNPs (nearly 10% of the Ec55989 chromosome). The alignment of those SNPs is available here.

I used SplitsTree4 to draw a phylogenetic network for the alignment. The result clearly shows that the outbreak genomes (green) are very similar to Ec55989 (also green, labelled ‘reference’), and very different to other sequenced E. coli (note in particular the group of EHEC O157:H7 on the middle left, which are very distant from the outbreak strains). Since this is a network, it would reveal if there were major recombinations between this strain and the other E. coli chromosomes, which there aren’t. This confirms that the outbreak strain is truly an EAEC, with very close similarity to Ec55989 and not to classical EHEC. This is backed by the presence of an EAEC plasmid, carrying aggregative adhesion fimbrial cluster I (AAF/I; agg operon; see this post for details).

I also used SplitsTree4 to draw a phylogenetic tree of the data, using K2P distances and the BioNJ tree-drawing algorithm. This shows the same result, with all three outbreak strain’s genomes clustering very tightly with the EAEC strain Ec55989.

So where are the SNPs located? This figure shows the distribution of SNPs around the Ec55989 chromosome:

SNPs relative to the Ec55989 chromosome

The outer blue rings indicate the Ec55989 genes encoded on the forward (outermost) and reverse strands. The purple/green wiggly line indicates the full set of SNPs found among all avaialable E. coli chromosomes in NCBI…this is a relative plot, so purple bits indicate where SNPs are relatively rare (lower density than the average across the genome) and green bits indicate where SNPs are particularly common (higher density than average across the genome).

The red ring indicates the location of SNPs that are shared among the three outbreak genomes compared to Ec55989, it that distinguish the outbreak from Ec55989 (~600 SNPs, or 0.12% divergence). For comparison, commonly circulating Salmonella enterica serotype Typhi, which are considered a tight-knit clonal group compared to other Salmonella and cause typhoid fever as opposed to the gastroenteritis caused by most Salmonella enterica, can differ from each other by >600 SNPs. So the outbreak strains should be considered quite closely related to the EAEC diarrhea strain Ec55989. In fact, some of these SNPs (red ring) are clustered together, suggesting that they actually represent variation in horizontally transferred sequences such as phage rather than genuine substitution mutations…so the real number of substitution mutations differentiating the outbreak strain from Ec55989 is probably more like 200-300, or 0.05% divergence.

The inner rings (green, blue, black) show the location of SNPs that are specific to just one of the three outbreak genomes. For the HPA and LB226692 strains these are VERY likely to be false SNP calls, due to the fact that they are reliant on Ion Torrent and 454 data which have trouble distinguishing between homopolymeric tracts (e.g. GGGTTT can easily be misread as GGTTTT, masquerading as a substitution of G->T). For TY2482, where we have Illumina read data to call SNPs (which doesn’t have this homopolymer issue), we find only 2 SNPs that are unique to Ty2482 compared to the other two outbreak strains (black inner ring). This is easier to believe, as we expect very small numbers of mutations to accumulate during the few weeks of evolution that separate these sequenced isolates.

E. coli data released under Creative Commons 0 license

BGI has now formally released their data, including Illumina reads, under Creative Commons 0 (CC0) license. This is the most open license possible, and includes this statement:

The person who associated a work with this deed has dedicated the work to the public domain by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.

They have set up this page with links to, and details of, all the data available. I understand HPA will do something similar.

This is an important step for crowdsourcing! In particular, it clarifies the position that this data is released for the benefit of the public & public health, and there are no restrictions on publication of data and analysis. This is important. The genomics community has long been used to pre-publication data release of ‘community resource’ genomics data (begun during the Human Genome Project), on the understanding (in broad terms) that the research community would not attempt to publish analysis of the released data until the data generators had completed their data generating and had a chance to publish their own analysis. In short, releasing data does not imply that others have the right to publish analysis of it. But the CC0 license does.

I’ve been asked by a few people about this, over the past week. There is understandably some discomfort about people publicly sharing analysis of data that they did not generate. My position has been that the data generators released their data freely for public analysis (with public health as the driver), and this is very different from a research project where they have released the data with the expectation that no-one will ‘publish’ anything before they themselves have. But it is important to formalise this…. I guess it just takes time for the legalities to catch up.

For the crowd-sourcers part, all the analysis posted at the GitHub E. coli O104:H4 Genome Analysis Crowdsourcing site will be available under the CC0 license too, this is being formalised today thanks to the folks at ERA7.