1. Genomic epidemiology of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa
Demographic expansion and genetic determinants of epidemiological success in a high HIV prevalence setting
Tyler S. Brown, M.D.
Columbia University Medical Center
Department of Medicine
@DrTSB
Good afternoon everyone, thank you [ ] for the introduction, and thank you for having me on the panel today
Let’s jump right in

2. Background
Extensively drug-resistant (XDR)-TB: Major threat to global TB control: costly and toxic drugs, high case mortality Resistance to isoniazid, rifampin, fluroquinolones, & 2nd-line injectables First XDR-TB isolate in KwaZulu-Natal (KZN) reported in 2001: Evolved from well-described multi-drug resistant strain LAM4/KZN Responsible for high-mortality outbreak among co-hospitalized HIV patients in Tugela Ferry ( ) High HIV prevalence setting (approximately 40%) Incidence: 3.5 cases of XDR-TB per 100,000 in Cases are geographically widespread across province Majority of new XDR-TB cases are due to transmission rather than de novo acquisition of drug resistance while on treatment
Extensively drug resistant TB poses a significant threat to global tuberculosis control
Treatment for resistant infections is costly, the medications used for treatment have significant side effects, and the case mortality associated with extensively drug resistant TB ranges between 50-80%
For reference, XDR-TB defined as resistance to isonaizid and rifampin, as well as fluroquinolones and 2nd line injectables, including kanamycin and streptomycin
The first XDR isolate identified in KZN was from 2001
Highlighted LAM4/KZN here as a strain to remember, and one that we’ll talk about quite a bit in this presentation
LAM4/KZN was responsible for a widely publicized high-mortality outbreak among hospitalized HIV patients
And of course important to underscore that HIV prevalence at antenatal clinics in KZN ranges 30-45%, which is higher than most of the rest of south africa

3. Research Questions When did XDR-TB emerge in KwaZulu-Natal?
What was the extent of transmission in the community before the Tugela Ferry outbreak? (Was Tugela Ferry a sentinel event or an initiating event for widespread community transmission?)
What pathogen-specific biological factors may have contributed to the emergence and spread of XDR-TB in KZN?
How did HIV co-infection and TB transmission in a high HIV prevalence setting influence the epidemiology and evolutionary history of XDR-TB?
Our work sought to answer three primary research questions
Was Tugela Ferry a sentinel event for transmission that was already ongoing in the community or an initiating event for widespread community transmission that would happen later?
Third, what role did HIV co-infection have in all of this?

4. Methods WGS  evolutionary history  epidemiological history
Whole genome sequencing, targeted sequencing, & RFLP-genotyping of Mtb clinical isolates from KZN ( )
Bayesian evolutionary analysis to infer phylogenetic trees and estimate dates for drug resistance mutations (time to most recent common ancestor)
Bayesian Skyline Analysis to estimate prior bacterial population history
Our approach to these questions used whole genome sequence data to reconstruct the evolutionary history XDR-TB, and then used that evolutionary history to look back in time and make inferences about epidemiological history
To do this we obtained whole genome sequence data on 296 TB clinical isolates, collected in KZN between 1994 and 2014
We then used Bayesian evolutionary analysis, a method I’ll talk about a bit more in a second, to estimate when in past each drug resistance mutation was acquired on the stepwise path from drug susceptible TB to XDR
To do this, we estimated the time to most recent common ancestor for groups of isolates sharing each important drug resistance mutation
We also used Bayesian Skyline Analysis to estimate the size of different TB populations going backwards into the past

5. Bayesian phylogenetic analysis
Here is an example from the HIV literature which applied Bayesian phylogenetic analysis to estimate when HIV was introduced to North America
The authors dated the most recent common ancestor of early HIV isolates from the US around 1970, suggesting that this was when HIV was first introduced to the United States

6. Bayesian Skyline Plot:
Estimates (bacterial) effective population size over time
The authors of this paper also used Bayesian Skyline analysis, which can generate estimates for the population size of your organism of interest (in this case HIV, in our case XDR-TB) going back into the past.
The dark line here, which you’ll see in our plots later, is the median estimated population size going back in time from the present and the shaded area represents the uncertainty around this estimate.
We use a software package called BEAST for all of these analyses

7. To get started on the results, here is a phylogenetic tree of our isolates, showing how related or unrelated different isolates are to each other

8. Mtb clinical isolates: 190 XDR
For this analysis, we compiled whole genome sequence data 190 XDR-TB isolates (marked here in orange), 54 MDR isolates (green), and 52 drug susceptible isolates

9. Mtb clinical isolates: 190 XDR 54 MDR 52 drug-susceptible
We compiled whole genome sequence data for 296 isolates, including 190 XDR-TB isolates (marked here in orange), 54 MDR isolates (green), and 52 drug susceptible isolates

10. Mtb clinical isolates: 190 XDR 54 MDR 52 drug-susceptible HIV prevalence 70-79% among XDR-TB cases Highly diverse: XDR-TB arose independently in 11 different RFLP strain families (distantly related) Majority of isolates are KZN/LAM
LAM4/KZN
HIV prevalence was 70-79% among XDR-TB patients
We identified XDR-TB across a diverse group of distantly-related TB strain families (marked here by their two-letter RFLP family names). We known that these RFLP strain families are separated by hundreds or even thousands of years of evolutionary history, indicating that XDR-TB emerged multiple times across multiple genetic backgrounds in KZN
Most isolates belong to the HP strain family, and most of the HP isolates are the aforementioned LAM4/KZN strain

11. Stepwise evolution of drug-resistance in LAM4/KZN katG (isoniazid): 1962 (1947-1972)
Next we sought identify when the LAM4/KZN XDR-TB strain evolved
In doing this, we were able to reconstruct the stepwise evolutionary process through which LAM4/KZN acquired each of its important drug resistance mutations
Here is the example of a particular mutation in katG that confers resistance to isoniazid. Node with green dot is at the TMCRA of all the isolates carrying this katG mutation (which here is estimated to be in 1962)

12. Stepwise evolution of drug-resistance in LAM4/KZN katG (isoniazid): 1962 ( ) inhA (isoniazid): 1975 ( )
Similarly we date a mutation in inhA promoter region, which is also associated with INH drug resistance, to have occurred some time between 1964 and 1983, most likely in 1975

13. Stepwise evolution of drug-resistance in LAM4/KZN katG (isoniazid): 1962 ( ) inhA (isoniazid): 1975 ( ) MDR (rpoB L452P  rifampin): 1985 ( )
The rpoB mutation that confers rifampin resistances, and marks the transition to MDR-TB, most likely occurred around 1985

14. Stepwise evolution of drug-resistance in LAM4/KZN katG (isoniazid): 1962 ( ) inhA (isoniazid): 1975 ( ) MDR (rpoB L452P  rifampin): 1985 ( ) XDR: rpoB D435G (rifampin drug-resistance) rpoB I1106T (not associated with DR) gyrA A90V (fluroquinolone resistance) rrs A1401G (kanamycin resistance) 1993 ( ) Consistent with prior estimates (Cohen, Abeel et al 2015)
To become XDR TB, LZM4/KZN acquired two different rpoB mutations,
a gyrA mutation conferring FQ resistance,
and an rrs mutation conferring kanamycin resistance
We date the most recent common ancestor for all 4 of these mutations to sometime around 1993
Will note that our estimates are consistent with some prior work on this topic

15. Effective bacterial population size (Ne) of HP/LAM4/KZN via Bayesian Skyline analysis
Ne increases 4-5 years before first reported XDR isolate
10 years before Tugela Ferry outbreak
Next we examined the size of the XDR-TB bacterial population going back in time using this aforementioned Bayesian Skyline analysis.
Some important dates on the timeline here
1993, which (from the last slide) is our WGS-based estimate for when this particular XDR-TB strain was built
2001, when the first XDR TB isolate from KZN was found in the lab
And the Tugela Ferry outbreak in 2005
We found that:
The XDR-TB bacterial population size expanded 4-5 years before the first XDR-TB isolate was found in the lab and 10 years before the TF outbreak
*TMRCA: time to most recent common ancestor

16. Effective bacterial population size (Ne) of HP/LAM4/KZN via Bayesian Skyline analysis
Ne increases 4-5 years before first reported XDR isolate
10 years before Tugela Ferry outbreak
Concurrent with increase in overall incidence of all TB cases in South Africa
*TMRCA: time to most recent common ancestor

17. Effective bacterial population size (Ne) of HP/LAM4/KZN via Bayesian Skyline analysis
Ne increases 4-5 years before first reported XDR isolate
10 years before Tugela Ferry outbreak
Concurrent with increase in overall incidence of all TB cases in South Africa
Concurrent with increase in HIV prevalence
“to underscore what this says” bold text/appear
*TMRCA: time to most recent common ancestor

18. Extensively drug-resistant subpopulations of M
Extensively drug-resistant subpopulations of M. tuberculosis expanded well before the Tugela Ferry outbreak, suggesting ongoing community transmission prior to detection via public health surveillance
To underscore again what these estimates show: we see that extensively drug-resistant subpopulations of M. tuberculosis expanded in size in KwaZulu-Natal prior to the Tugela Ferry outbreak and before the first XDR-TB isolate from 2001 was identified, suggesting ongoing community transmission well before these strains came to the attention of clinicians and well before these strains were detected via conventional drug susceptibility testing

19. Limitations
Cross-sectional sampling may under-represent non-LAM4/KZN XDR isolates if these isolates are clustered in under-sampled populations, or if infections with these isolates are less likely to cause fulminant clinical disease
Effective population size is at best a proxy measure for incidence or total number of infections
Two few important limitations of our study
1. Cross-section sampling may have under-represented non-LAM4/KZN strains, for example if they were clustered in under-sampled populations
2. Be conservative about effective population size and acknowledge that it is a proxy measure for incidence

20. Summary When did XDR-TB emerge in KwaZulu-Natal?
Most likely in 1993, 8 years before the first isolate reported in KZN and 12 years before the Tugela Ferry outbreak.
Bacterial population expansion before , suggesting ongoing transmission of this strain before detection via public health surveillance
What pathogen-specific biological factors may have contributed to the emergence and spread of XDR-TB in KZN?
Unique set of rpoB mutations in the epidemiologically successful KZN/LAM4: ?selective advantage associated with additional rpoB mutation(s)
How did HIV co-infection and transmission in a high HIV prevalence setting influence the epidemiology and evolutionary history of XDR-TB?
Close temporal association between increasing HIV prevalence, increasing incidence of all TB cases, and XDR-TB bacterial population size. HIV likely facilitated the spread of XDR-TB
‘Taken together, it’s not unreasonable to believe that HIV likely facilitated the spread of XDR-TB’

21. Implications
HIV treatment likely a cornerstone to preventing further spread of XDR-TB
Prioritize pharmacosurveillance, include new anti-TB medications (bedaquiline, delamanid) in drug susceptibility testing now
Evolution happens fast (even in TB): whole genome sequencing is an important tool for early detection of emerging drug resistance
What are the implications of this work
First, and this is news to no one, these findings underscore the importance of HIV treatment as a cornerstone for preventing further spread of XDR-TB
Second, we need to look aggressively and early for emerging drug resistance mutations, which means we should start looking now for resistance to the novel TB antibiotics bedaquiline and delamanid
Last, we should acknowledge that evolution of drug resistance happens fast, and promote whole genome sequencing as an important tool for the early detection of emerging drug resistance mutations

22. Acknowledgements
Sergios-Orestis Kolokotronis
Barry N. Kreiswirth
Sara C. Auld
James C.M. Brust
Shaheed Omar
Pravi Moodley,
Apurva Narechania
Neel R. Gandhi
N. Sarita Shah
Barun Mathema*
Kristin N. Nelson
Nazir Ismail