Hello, I'm Sarah Tallarico. I'm an epidemiologist with the molecular
epidemiology activity in the division of tuberculosis elimination at CDC. I will
be presenting the following set of training slides focused on the use of
whole genome sequencing or WGS for investigating recent TB transmission
along with colleagues, Tambi Shaw and Martin Cilnis, from the TB control
branch at the California Department of Public Health. At the end of this
presentation, participants will be able to describe: Key differences between
conventional genotyping and WG: What is being represented on a phylogenetic tree:
How WGS is used to assess whether patients are potentially linked by
recent transmission: Why WGS alone cannot be used to infer direction of
transmission: And how TB control programs can use WGS analysis in an investigation.
This presentation is divided into three parts.
Part one is an introduction to using WGS for detection and investigation of
recent TB transmission. I will cover the goals of TB molecular epidemiology,
current genotyping methods, use of WGS for investigating TB transmission, and a
guide for interpreting results of WGS analysis. Part two will be case studies
using WGS to investigate TB cluster alerts in California. Martin and Tambi
will present two case studies with WGS and epi data. One is a confirmed outbreak
and one is a refuted outbreak. Then in part three, I will briefly describe the
plans for transition to universal prospective WGS. A separate presentation
covering the details of how universal prospective WGS will be implemented will
be made available in the future. First, I will go over some background for
how to use WGS for detection and investigation of recent TB transmission.
TB is caused by Mycobacterium tuberculosis and is transmitted through
the airborne route. 5% of people who are infected develop active TB disease
within two years. These people are infectious and can carry on the chain of
transmission. The other 95% developed latent TB, which is not infectious.
However, about 5% of people with latent TB will reactivate and develop active
disease at some point in their life. So two main strategies for eliminating TB
are detecting and treating latent TB infection and detecting and interrupting
ongoing transmission, which is the focus for today's presentation. TB molecular
epidemiology targets recent transmission with the goal of reducing the burden of
TB, by identifying where transmission is currently occurring and interrupting it.
The challenges we need to distinguish TB cases that are due to recent
transmission from cases that were infected long ago and are just now
developing active disease. We do this by combining molecular, clinical and
epidemiologic data to detect, investigate and monitor recent transmission. We
combine molecular data with clinical and
epi data because there are challenges when it comes to trying to rely exclusively on
epi data to investigate TB transmission.
The fact that transmission is airborne can make it difficult to assess exposure.
Assessing exposure in congregate settings can be very complex as well. TB also can
have infectious periods that span years and for that reason, patient recall may be incomplete or unreliable.
And TB transmission often occurs in impoverished or marginalized communities
that are difficult to access. For these reasons, it is helpful to use genotyping
which can provide additional complementary information to aid
detection and investigation of transmission by identifying cases with
genetically similar M tuberculosis isolates that are more likely to be
linked by transmission. Genotyping examines the DNA of M
tuberculosis isolates from TB patients. The M tuberculosis bacteria from a TB
patient is called the patient's isolate. Bacteria, including M tuberculosis have
DNA called a bacterial genome. DNA is made up of four different nucleotides
abbreviated (A, T, C and G). The order of these nucleotides in the genome is the
DNA sequence. The genome of M tuberculosis is over 4.4 million
nucleotides long. Genotyping can be used to identify TB patients who are more
likely to be linked by recent transmission. Changes in the DNA, called
Mutations, occur over time so M tuberculosis bacteria don't all have the
exact same DNA sequence. At the time of infection, the person transmitting the
infection and the person acquiring the infection will have M tuberculosis with
identical DNA sequence. Genotyping analyzes DNA to identify TB patients
with similar M tuberculosis genomes who are more likely to be linked by recent
transmission. In this schematic, transmission is occurring between these
two people at the top and they have M tuberculosis isolates with the same
genotype (shown in black), but this person at the bottom is not part of that
transmission chain and has an M tuberculosis isolate with a different
genotype (shown in green).
CDC uses M tuberculosis genotyping data to detect clusters of possible recent
transmission. Two or more isolates with the same genotype are considered
clustered. This schematic on the right is showing M tuberculosis genotypes in a
community, and we can identify a green cluster and a black cluster. Algorithms
that consider time and space are then used to identify clustered cases that
may be due to recent transmission. And CDC has developed clustered detection
methods for this purpose. One method is the LLR cluster alert that detects an
unexpected increase in concentration of a genotype in a jurisdiction during a
three-year time period. Another type of alert is for surveillance of large
outbreaks, defined as 10 or more cases in a three-year period related by recent
transmission. Per an M tuberculosis genotyping methods cover only about one
percent of the genome and are based on differences in repetitive regions within
the M tuberculosis genome. The current method combines the results of two
assays, spoligotyping and MIRU VNTR, to give an M tuberculosis GENType.
Specifically, spoligotyping is based on the presence or absence of 43 spacer
sequences in a direct repeat region of the genome. And MIRU VNTR is based on
differences in the number of copies of tandem repeats at 24 regions or loci of
the genome. Isolates that have the exact same spoli-
gotype and 24 locus MIRU VNTR pattern are assigned the same GENType. However,
GENTyping provides relatively low resolution for examining the genetic
relatedness of isolates because it only examines a small portion, about 1% of the
genome. The regions of the genome examined by gentyping may not change
within a time frame that is useful for understanding recent transmission. This
makes interpretation difficult when there has been substantial past
transmission of a GENType in a community because it is harder to
distinguish cases due to reactivation of infection that was acquired during the
past transmission versus cases due to recent transmission. And it is harder to
distinguish separate chains of recent transmission among cases with the same
GENType. WGS can provide added resolution for examining genetic
relatedness of isolates by expanding coverage of the genome to about 90
percent compared to the 1% that is covered by gentyping. This captures much
more of the genetic changes that occur. CDC has been performing WGS and
phylogenetic analysis retrospectively for select GENType clusters and has
analyzed more than a hundred clusters nationally to date. 2012 is when we first
did WGS of a GENType cluster. In 2014, we started performing WGS for all GENType
clusters that alerted for large outbreak surveillance. In 2016, we expanded WGS
capacity to include other selection type clusters that could inform public health.
This map is showing the number of isolates with whole-genome sequencing
data for each state or territory with a total of 2,776 isolates having been
sequenced as of August 2017, but retrospective sequencing is still
ongoing. The whole-genome sequencing data is used to perform a whole-genome single
nucleotide polymorphism analysis (or WG SNPs analysis). A single nucleotide
polymorphism (or SNPs as we call them) is a mutation at a single
position in (an A, T, C or G) in the DNA sequence. wgSNP analysis uses WGS data
to identify SNPs that are useful for examining the genetic relationship among
Isolates. SNPs that are identified in the wgSNP analysis are mapped on to a
phylogenetic tree to diagram the genetic relationship among isolates. The
phylogenetic tree can be used to focus and inform epidemiologic investigation
of the cases. wgSNP analysis is done by first aligning the isolate sequence
reads to a reference genome. We use M tuberculosis H37RV. This is shown on
the right where the sequence reads for the isolate are in black and they are
being matched up to the sequence of the reference genome shown in gray. Then
SNPs relative to the reference genome h 37 RV are identified. This is shown here
where the isolate sequence has an a at this position where H37RV has a T. Then
the next step is that uninformative and unreliable SNPs are filtered out to
produce a list of high quality SNPs. What we filter out are SNPs that are
present in all the isolates in the analysis (because if the SNP is present
in all the isolates in the cluster being analyzed, the SNP is not informative for
understanding the genetic relationships among the isolates). We also filter out
SNPs that are identified because of assembly errors, (which means the sequence
read wasn't aligned to the correct place of the reference genome). And low
confidence SNPs (for example if there are very few sequence reads that cover
the SNP position). Then lastly the high quality SNPs are mapped on to a
phylogenetic tree to diagram the genetic relationships between the isolates.
Here is a guide for interpreting the phylogenetic tree. On this tree the
isolates are shown as circles called nodes (these would usually be labeled
with the isolates accession number). Isolates that have the same genome type
are displayed together in one node. Nodes are connected by lines that are
proportional in length to the number of SNPs that differ between the isolates.
And the lines are labeled with the number of SNPs. You will see that the
tree also has a node labeled MRCA. MRCA stands for most recent common ancestor.
The MRCA is not an actual isolate but a hypothetical genome type from which all
isolates on the tree are descended. And so the MRCA serves as a reference point
for examining the direction of genetic change which is shown with these blue
arrows. On this tree, if we start up top at the MRCA
and move down this branch, on the right there are three SNPs shown in yellow
and this isolate has those three SNPs. But if we were to move down this other
branch, on the left we come to this first node that has two isolates and these two
isolates don't have those three yellow SNPs. But they have two different SNPs
shown in blue. And then from there we can move up the branch and there is an
isolate that has those same two blue SNPs plus one more SNPs shown in purple.
If we start back at the node with the two isolates and move down the branch,
the two isolates down at the bottom have the two blue SNPs plus two red SNPs.
And then each one has one additional SNPs as well. One has a green SNPs and
one has a dark blue SNPs. And you will see at the bottom of the tree that trees
also sometimes have a branching point with no circle. This is called a
hypothetical node. The hypothetical node represents a hypothetical genome type
but there is no actual isolate with this genome type in the analysis. So in this
example if we start at the node with the two isolates with the two blue SNPs and
move down this branch to the hypothetical node, you see that the
genome type at the hypothetical node has the two blue SNPs
and the two red SNPs but we don't actually have an isolate with that
genome type in the analysis. We use the tree to examine the number of SNPs that
differ between the isolates and identify groups of closely related isolates that
may be involved in recent transmission. As well as to identify genetically
distant isolates that are unlikely to be involved in recent transmission.
This helps programs prioritize cases for focus of their epi investigation. SNPs
thresholds for categorizing M tuberculosis isolates as genetically
distant or closely related have not been formally established for
CDC's wgSNPs analysis yet. Based on CDC's general experiences using wgSNPs
analysis for investigating recent transmission isolates with zero to five
SNPs, differences are considered closely related and isolates with six or more
SNPs differences are considered genetically distant. SNPs thresholds will
vary depending on the methods used for the wgSNPs analysis and cannot be
compared to thresholds used by other groups with different analysis methods.
These recommended SNPs thresholds may change as CDC's wgSNPs analysis
methods are further developed or based on results of a formal validation
analysis of SNPs thresholds. An important consideration to keep in mind when
looking at these trees is that the phylogenetic trees are not the same as
transmission diagrams because directionality of transmission cannot be
inferred from wgSNPs analysis alone. If we look at the simple tree on the left
isolates A and B are identical and it's possible that patient A could have
transmitted to patient B or patient B could have transmitted to patient A.
Directionality of transmission also cannot be inferred because there could
be cases involved in transmission that are not included in the WGS analysis. For
the same tree it is possible that there is no transmission between patient A and
B and transmission was through a third case that does not have an isolate on a
tree because they are an undetected case over
culture-negative. Another consideration is there could be genetic changes that
occur between the time of transmission and collection of the patient sample.
With this tree it is tempting to think that patient A transmitted to patient B
since isolate B is shown to have evolved from isolate A. That could be true but it
is actually still possible that patient B could have transmitted to patient A. To
illustrate this concept with an example, patient B could transmit to patient A in
2015, so both patients have the same genome type in 2015. Patient A sample is
collected about 1 year later and no mutations have occurred during that time.
Patient B sample is collected even later and during the time period between
transmission and collection of the sample, patient B could have a mutation.
Here a T2G in his infecting strain which would result in a tree that looks like
this on the left. For these reasons we don't use the trees to try to infer
transmission between patients. We use them to identify clusters of cases that
may be due to recent transmission. It is also important to remember that it is
easier to rule out recent transmission then confirm it using WGS. Even isolates
that are closely related or identical by WGS can be due to reactivation. This is
because mutations may not occur as frequently during latent infection and
therefore, SNPs may not accumulate. So the phylogenetic tree should be used in
conjunction with clinical and epidemiologic information to assess
recent transmission. In summary WGS can provide greater resolution than GENType
for investigating recent TB transmission. CDC has used WGS retrospectively to
examine genetic relatedness of isolates clustered by GENType. wgSNPs analysis
is performed to produce a phylogenetic tree for examining genetic relationships
between isolates and a GENType cluster and the phylogenetic tree
should be used in tandem with epidemiologic data to identify clusters
of closely related isolates that may indicate recent transmission. Not to draw
conclusions about direction of transmission among individual patients.