Gawande PQE 5_6_11.pdf

8/10/2019 Gawande PQE 5_6_11.pdf

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FUNCTIONAL CONSEQUENCES OF VARIABILITY IN

MYCOBACTERIUM TUBERCULOSISAT THE HOST-

PATHOGEN INTERFACE

Richa GawandePreliminary Qualifying Exam

Biological Sciences in Public Health

05.06.11


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Variability at the Host-Pathogen Interface in Mtb Richa Gawande // 05.06.11

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This proposal investigates variability at the host-pathogen interface and associated functional consequences

duringMycobacteriumtuberculosis(Mtb) infection.

Abstract and Significant AimsThe course of Tuberculosis (TB) disease is highly variable: most infected people never experience

active disease, while some develop progressive, primary disease soon after infection and others develop activedisease long after infection. While heterogeneity in host susceptibility contributes to the spectrum of outcomes

in TB infection, the trigger for reactivation, in particular, is often unknown1. Patients inhale only a few bacilli

at the time of infection, and variability in the virulence properties of these bacilli could modulate early events in

infection2. This is particularly significant if this variability translates to a functionally different early immune

response, as it could help explain differences in clinical outcome.

I propose that there exist bacterial components at the host-pathogen interface that modulate early eventsin infection. I will focus on features of the bacterial outer cell wall that are known to physically interact with

the host. In particular, a family of large, complex lipids of the outer cell wall is unique to pathogenicmycobacteria and has been associated with strain-based differences in virulence

3. Two members of the family

phthiocerol dimycocerosate (PDIM) and the Mtb-specific phenolic glycolipid (PGL) are important forvirulence in mouse models

4,5,6. For PDIM, we have characterized a frameshift mutation in its biosynthetic

locus. This mutation abrogates PDIM expression and occurs at high frequencies in vitro7. I propose that thisevent also occurs in vivo, and may translate to a heterogeneous population of bacilli that gets transmitted from

host to host. Here, I aim to further characterize heterogeneity of PDIM expression in infected hosts, and toforge a link between cell-to-cell heterogeneity in PDIM and heterogeneity in the immune response.

More stable genetic differences among Mtb strains may result in strain-based differences intransmission, lethality, immune modulation, and drug resistance8. For example, some strains associated with

hypervirulence in the East Asian lineage produce PGL and some strains, including those in the Euro-Americanlineage, do not

9. While PGL variability is one functional consequence of strain-based genetic diversity, many

interacting bacterial genetic factors may lead to functional variability in clinical presentation. Here, I propose acomprehensive approach to identify genetic requirements for growth for different clinical strains during mouse

infection. Further, I will focus on variability in requirements for, and production of, surface lipids at early times

in infection as a possible functional consequence for strain-based genetic diversity.Together, these experiments aim to determine the effects of genetic heterogeneity in Mtb virulence

factors on early events in infection.

Aim I: To test the hypothesis that Mtb can tune its early interactions with the host through high

frequency PDIM variability.

(1) I will identify a PDIM-specific cytokine fingerprint in infected macrophages that distinguishes between

PDIM(+) and PDIM(-) strains. (2) I will determine the single-cell immune response to PDIM(+), PDIM(-), andmixed infections, using fluorescent TB strains and fluorescent macrophage reporters. (3) I will measure PDIM

variation in single Mtb colonies derived from patient sputum by PCR resequencing and TLC analysis.

Aim II: To test the hypothesis that clinical strains from different lineages possess functionally differentrequirements for growth in vivo.

(1) I will conduct transposon-site capture and sequencing (TraCS) to identify genes that are differentiallyrequired for growth in mouse infections across clinical strains. (2) By lipidomics, I will compare the in vitro

lipid profiles in PGL-producing and -lacking strains. Together with the TraCS data, I will determine whetherPGL loss predicts in vivo essentiality and in vitro production of other surface lipids.


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Background

MTb, the causative agent of TB, is a unique pathogen highly divergent from model systems.Characterized by a thick, waxy cell wall, slow growth, and unique modes of virulence, MTb infection leads to

complex disease progression, which can be extremely difficult to diagnose10,11

. Infection can progress to activedisease, wherein bacteria can be aerosolized and efficiently passed on to a new host; upon inhalation of a few

bacilli, MTb is taken up by alveolar macrophages, and can establish its long-term phagosomal niche12,13.Mouse models have helped reveal many of the immunologic parameters required to control TB

infection. Upon exposure to Mtb, dendritic cells (DCs) migrate to the nearest draining lymph node and activateT cells

14,15. Infected macrophages activated by T cells deploy numerous innate immune effectors to quell

bacterial replication, including reactive oxygen species, antimicrobial peptides and TNFalpha, which, incoordination with the adaptive immune response, contributes to formation of the granuloma

16. In most

individuals, this process is efficient at controlling Mtb replication, which slows upon induction of the adaptiveimmune response, but doesnt result in sterilizing immunity, suggesting that Mtb possesses strategies to subvert

the immune response17

. Indeed, Mtb exhibits an exquisite capacity to evade the early macrophage assault,blocking phagolysosomal fusion, and forcing a balance of pro- and anti-inflammatory factors

18,19,20. Although

the exact molecular mechanisms underlying these host-pathogen interactions are not well-characterized, thecomplex lipids located in the outer cell wall of Mtb are thought to play a role in important events such as entry

and blocking phagosomal maturation21,22,23. The high lipid content of the mycobacterial cell wall makes itunique among other bacterial pathogens, and many of these lipids are critical determinants of mycobacterial

virulence4,5,24,25

.Here, I focus on two cell wall lipids important at the host-pathogen interface, PDIM and PGL, whose

production is restricted to pathogenic mycobacteria8,26,27

. PGL and PDIM share the same lipid core, and theirbiosynthesis and transport genes, encoding several multi-functional polyketide synthases, are clustered together

in the TB genome12,28

. PDIM mutants are attenuated from early on in mouse infections and reach a lowerplateau in the chronic phase of infection compared to wild-type Mtb

5,6. In a competition experiment, the

PDIM(-) strain exhibits lower fitness compared to the PDIM(+) strain, though not all PDIM(-) mutants arecleared (Figure 2, our lab). Infection of murine bone-marrow derived macrophages (BMDMs) with

PDIM(-) strains provokes an increased pro-inflammatory response compared to wild-type strains, suggesting

that PDIM helps Mtb evade the innate immune response29. Production of PGL is also unique to pathogenicmycobacteria such as Mycobacterium leprae, but is only produced in a limited number of Mtb strains

30.

Infection of mice with PGL-producing Mtb results in significantly reduced survival, and in BMDMs, reduction

in pro-inflammatory cytokines31,4

. This experimental data has been corroborated in clinical settings, where W-Beijing strains producing PGL are associated with epidemic spread, disseminated disease, and increased drug

resistance32,33,34

.The loss of PGL production through a 7-bp deletion anchors the divergence of the Euro-American lineage

from the remaining lineages in the Mtb complex (MTC) (Figure S1)9. The MTC includes all strains of Mtb andseven related mycobacterial species. Despite the ~99.9% genomic similarity characterizing the MTC, the globa

population of Mtb shows significant functional diversity in host tropism, clinical presentation of disease, anddrug sensitivity profile

,35. The globally dominant W-Beijing strains produce PGL; these lipids may contribute

to functional consequences in these strains. The population structure of the MTC, defined by genetic patterns ofinterspersed repetitive units, large sequence polymorphisms, and SNPs, consists of six main lineages, which are

geographically structured36,37. A sub-phylogeny of 100 diverse Mtb isolates suggests strong, stable associationsbetween Mtb strains and distinct geographic regions and host populations

38,39. As such, different host

environments may have contributed to the genetic divergence and evolution of these strains, resulting in geneticdifferences that are linked to unique mechanisms of survival.

The propensity to create genetic heterogeneity is limited in mycobacteria, which lack a mismatch repair(MMR) homolog but seem to compensate for this deficiency, exhibiting a very low base substitution rate

(similar to the MMR-positiveEscherichia coli)40,41

. One way mycobacteria may compensate is by reducing


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repetitive regions of the genome which are thought to be structurally unstable and prone to frameshift mutationswhich are repaired by MMR in other organisms

42. However, we still find such repetitive regions in the genome,

in particular, in the pps operon which encodes the phthiocerol portion of PDIM (Figure 1). Using an in vitroframeshift reporter, we measured the in vitro mutation rate of this sequence, and other repetitive sequences. In

the closely related Mycobacterium smegmatis, we found that the ppsA sequence mutates by the predicted +1mechanism at a rate 40X higher than the base substitution rate (Figure 3). I propose that this repetitive region

may be a high frequency mechanism for generating TB bacilli with differing capacities for in vivo virulence.The ability to diversify virulence determinants on a cell-to-cell and inter-strain basis is an important

strategy for many human pathogens43,44,45

. Very little is known about the mechanisms by which Mtb achievesfunctional diversity, and whether and how this diversity arises within an infected host. Through the proposed

work, I hope to understand how clinically-derived populations of Mtb generate functional variation in virulence,in an effort to understand the wide spectrum of TB disease.

Preliminary Data

__________________________________________________________________________________________Figure 1. ppsA mutation occurs in a repetitive region of the genome. The PDIM biosynthetic locus of

H37Rv. The ppsA repeat region occurs roughly in the middle of the ppsA gene, and the +1 mutation abrogatesPDIM expression.

__________________________________________________________________________________________

Figure 2. In a mixed competition, PDIM-negative strains are attenuated, but persist through 9 weeks.

MTb isolates from murine lung infections were sequenced for ppsA at varying weeks post infection. Ratio of

ppsA mutant/WT decreased significantly over time, though mutants persisted at 12% at 9 weeks. N indicatestotal number of lung isolates sequenced.


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Construct tested

__________________________________________________________________________________________

Figure 3. ppsA repeat has a 40X higher mutation rate than the base substitution rate, as calculated by an

in vitro frameshift reporter. Sequences of varying length were inserted upstream of the Kanamycin-resistance gene such that an insertion of +1 restores Kan resistance, otherwise the cell remains susceptible.

Fluctuation analysis was conducted to determine mutation rate.46

Research Design and Methods

Aim I: To test the hypothesis that Mtb can tune its early interactions with the host through high frequency

PDIM variability.

Aim 1A: To characterize the immune fingerprint which distinguishes between PDIM(+) and PDIM(-) at

early times in infection.

Previous studies suggest that PDIM helps Mtb evade the innate immune system29

. To determine howthe innate immune response differs in the absence or presence of PDIM, I will measure bulk cytokine

production of macrophages in response to PDIM(+) and wild-type H37Rv, and PDIM(-) H37Rv which containsthe ppsA frameshift mutation, at varying times post-infection.

I will use the RAW 246.7 murine macrophage-like cell line to measure cytokine response to PDIM(+)and PDIM(-) H37Rv infection. RAW 246.7 cells are widely used to study of signaling pathways and

inflammation47

. I will conduct pilot infection studies to optimize this system to capture the initial cytokineresponse to H37Rv infection. Based on the literature, I will infect RAW 246.7 cells at MOIs of 1:1 to 5:1

bacteria per cell with PDIM(+), PDIM(-) H37Rv in triplicate. I will potentially stimulate infected macrophageswith IFNgamma based on pilot studies. I will also maintain uninfected, activated cultures to control for

baseline cytokine expression, as well as cultures infected with an H37Rv mce mutant known to be


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hypostimulatory, to control for specific cytokine response. I will collect and filter culture supernatants at 4hours post infection, and 1, 3, and 5 days post infection. Macrophages will be lysed for CFU enumeration at

these times.I will measure cytokine production by standard sandwich ELISA assay. Measuring not just pro-

inflammatory cytokines but also immunomodulatory cytokines will allow me explore how PDIM helps silencethe immune response. I will measure the proinflammatory cytokines TNFalpha, Il-6 and IL12p40, and IL-18,

immunomodulatory cytokines such as IFNalpha, IFNbeta and IL-10, the inflammasome-mediated IL-1beta, andchemokines important for DC migration such as CCL3.

Anticipated Results and Limitations

I will determine whether PDIM(+) and PDIM(-) strains can differentially stimulate the innate immuneresponse in RAW 264.7 cells, compared to the uninfected, activated macrophages, and macrophages infected

with a hypostimulatory strain of Mtb as a control. The cytokines whose expression is most significantlydifferent between PDIM(+) and PDIM(-) infections (calculated by students t test), will be selected as the

PDIM-specific signature. Previous results from BMDMs indicate that TNFalpha may be a strong part of thissignature; the data from the above experiments will confirm and expand upon this result. A decreased release

of TNFalpha in PDIM(+) cells may be an important strategy for escaping initial host control of the infection.PDIM has been implicated in bacterial cell wall permeability; as such, loss of PDIM may change the

composition of the cell wall, exposing different molecules at the host-pathogen interface51. On the other hand,PDIM may be actively suppressing a pro-inflammatory response, for example, by skewing the receptor that

PDIM(+) bacilli use for entry. Production of anti-inflammatory cytokines will indicate whether PDIM may beactively inducing these cytokines. Upon measuring the cytokines listed above, I could use a semi-global

cytokine array to achieve better resolution of the full macrophage response to PDIM. Finally, it is possible thatPDIMs role is not to interact with the host at all, but to preserve Mtb in macrophages; in its absence, Mtb may

significantly weakened on the whole, leading to better control by macrophages.I anticipate that the technical details of the proposed experiments will not present any major challenges,

as the macrophage culture, infection, and immune assays are established methods in our lab. It may benecessary to switch or confirm results in other cell types, such as murine BMDMs or human monocyte-derived

macrophages.

Aim IB: To test the hypothesis that PDIM is a tuner of the host immune response at a single cell level.

Single cells lose PDIM over the course of infection through the slip-stranding mechanism proposed

above, thus PDIM variability is most likely to occur on the cell-to-cell level in infected hosts. Previous studieshave shown that macrophages respond differently to virulent and avirulent strains48. Similarly, the experiments

proposed above provide a rough-grain picture of whether PDIM can module innate immune cytokines in bulk.However, dominant signals released by a few activated macrophages may influence the cytokine response of the

entire macrophage population, and may not reflect the cell-to-cell variability associated with both bacterial andhost factors. While deconstructing dynamics of these activation signals is beyond the scope of this proposal, it

is important to examine additional dimensions when predicting how a mixed infection may modulate theimmune response. To characterize the differences in immune response to PDIM(+) and PDIM(-) bacteria, I aim

to answer the following questions. 1) Can the single-cell immune response distinguish between PDIM(+) andPDIM(-) infections? 2) Is the PDIM(+) response dominant? That is, is the single-cell response to a mixed

infection skewed towards a purely PDIM(+) or PDIM(-) infection, or does it reflect an intermediate level?I will use transcriptional reporters for innate immune genes to determine the immune response to

PDIM(-) and PDIM(+) infections at the single-cell level. Beaulieu et al have constructed fluorescence-basedpromoter-reporter constructs to measure single-cell responses to Mtb infection in RAW 264.7 cells

49. These

reporters include AmCyan-tagged TNFalpha promoter and EYFP-tagged IL-6 promoter. I propose to use thesereporters to measure the PDIM-specific single-cell response to Mtb infection. My bulk analyses will indicate


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which of the existing immune reporters has the potential to distinguish between PDIM(+) and PDIM(-)infection. I will select the best among these to establish the baseline single-cell system.

PDIM(+) and PDIM(-) strains will be marked with constitutively expressing fluorescent proteins such asGFP and mCherry, which we routinely use in our lab. Image acquisition and analysis will be done using our

DeltaVision wide-field fluorescence microscope and appropriate software, which others and I in the lab haveoptimized.

To answer the question of whether PDIM can modulate the cytokine response at a single cell level, I willinfect the reporter cells with fluorescently marked PDIM(+), PDIM(-), or an mce mutant of H37Rv. Based on

and using conditions developed in Aim IA, I will assess a range of activation methods to characterize the singlecell response to macrophages infected with these strains. Cells will be fixed at appropriate timepoints and

fluorescence intensity measured to ascertain per-cell cytokine transcription as it relates to bacterial type andburden. I predict that there will be underlying heterogeneity in cytokine transcription independent of PDIM

status, whose effect I will estimate from the uninfected, activated macrophage distribution. I will use thisdistribution to threshold and normalize the infected distributions to ensure consistent comparisons across

samples.To determine whether the PDIM(-) immune response is dominant in a PDIM(+)/PDIM(-) mixed

infection, I will infect macrophages with equal amounts of GFP-marked PDIM(+) and mCherry-markedPDIM(-) H37Rv, at an MOI of roughly 2:1 bacteria/macrophage. Based on the analyses above, I will determine

whether the PDIM (+) or PDIM(-) immune signature dominates in these mixed infections. Macrophages in thesame culture dish infected with only one strain will serve as a reference in addition to the experiments above.

As above, the analysis here must include subtraction of background heterogeneity in uninfected, activatedmacrophages, as well as normalization for number of bacteria per cell.


The single-cell level data will provide one of the first insights into whether heterogeneity in the bacterialpopulation can direct heterogeneity of the early innate immune response. I anticipate that pure populations of

PDIM(+) or PDIM(-) Mtb will elicit a similar response trend on the single-macrophage level as they did on thepopulation level. Analysis of the single-cell distributions will reveal additional dimensions. For a given

infection, variance in fluorescence intensity will reflect how much variability exists in the cytokine response

across a population of macrophages. Temporal shifts in fluorescence will reveal kinetics and signal processingof cytokine transcriptioni.e., whether macrophages produce cytokine at once, or in a staggered manner. Ipredict that the kinetics will reflect some combination of intracellular and extracellular signals, but may be more

intracellularly driven for PDIM(-) compared to PDIM(+) infections due to the defect in immune evasionreported of PDIM(-) strains.

The mixed infection experiment could help determine the role of PDIM in infection. For example, ifPDIM is inhibiting a signaling pathway or receptor, its presence may be sufficient to shield a PDIM(-) bacillus

in the same macrophage. However, the intracellular dynamics are likely to be complex due to differentialphagosome localization and different receptors used. If necessary, I will stain with LysoTracker to obtain

information about phagosomal localization.The use of macrophage reporters has been well-established in other systems, and I dont anticipate

significant challenges in adapting these reporters to our system. However, underlying noise will be the majorchallenge in extracting a clean, PDIM-specific signal. It will be important to limit our immune readout to one or

two major cytokines, and to threshold our results based on the uninfected, activated macrophages as a negativecontrol. If necessary, I will apply dimension reduction techniques such as principal component analysis to

identify which condition(s) best explain the variance in my data.


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Aim 1C: To measure PDIM variation in Mtb derived from sputum of infected hosts.

The above experiments lay out a role for PDIM at the early host-pathogen interface, which is currently

unknown. In addition, the single-cell experiments may reveal a relationship between Mtb populationheterogeneity and host response heterogeneity. The unstable region in the PDIM biosynthesis locus, the high

energy cost involved in synthesizing PDIM, and the possibility that requirements for PDIM may be temporallyrestricted, suggest that a mixed population of bacilli may exist in an infected host. The PDIM biosynthetic

locus contains an unstable repeat region, which is prone to frameshift mutations (Figure 1). From workdescribed in Figure 2, the ppsA region offers a genetically tractable method for measuring PDIM variation in an

infected host. From these measurements, we found that indeed, PDIM(-) bacilli persist in infected mice. Todetermine whether PDIM variation exists in infected hosts, particularly at the transmission bottleneck, I propose

to measure PDIM expression in Mtb directly from patient sputa.I propose to directly plate smear-positive human sputum onto 7H10 plates to allow growth of single

mycobacterial colonies. I will plate undiluted sputum and serial dilutions to assure single colony resolution. Iwill include uninfected sputum spiked with PDIM(+) and PDIM(-) Mtb as controls. I will then pick a single

colony and conduct single-colony PCR to amplify an approximately 600bp region of ppsA encompassing therepeat region. I have successfully PCR amplified and sequenced the ppsA region from ~ 300 mycobacterial

colonies from mouse lung isolates using this approach (Figure 2). Multiple sequence alignment of the regionsurrounding the ppsA repeat in sequenced clinical strains show that this region is well-conserved, and thus I

anticipate that the H37Rv ppsA primers will also work for the strains isolated from human sputum. Colonieswhose sequencing traces contain the frameshift mutation in the ppsA repeat region will be considered evidence

for PDIM loss inside the infected host. For a subset of these colonies, I will perform chloroform-methanolextractions to isolate cell wall lipids, and will measure PDIM production by thin-layer chromatography (TLC)

using standard methods.We have an ongoing collaboration with investigators in South Africa and Tanzania. Our collaborators in

Tanzania have generously provided us with 12 smear- and culture-positive frozen sputum samples, for whichour lab has obtained approval from the Harvard Committee on Microbiological Safety to use for research

purposes. I will conduct pilot studies on this limited supply of sputum. If I am able to successfully amplifyppsA from these sputum-derived colonies, I hope to travel to Durban, South Africa to have access to a greater

number of smear-positive sputum samples.


PCR of the ppsA repeat region from colonies derived from human sputum will indicate whether

heterogeneity in the ppsA region arose within the infected host. Sequencing traces will show whether a ppsAmutant (ppsA(mut)), ppsA wild-type (ppsA(WT)), or mixed population existed in the colony which was PCR

amplified. From mutant accumulation studies from our lab, the likelihood of a frameshift mutation occurringduring the growth of a single PDIM(+) bacterium into one colony (requiring approximately 10 replications) is

nearly zero. In any case, the maximum percentage of revertants (either ppsA(mut) to ppsA(wt) or vice versa)arising from a single bacterium growing into a colony is 50%, which can be detected by PCR followed by

sequencing (preliminary data, our lab). Therefore, I will consider any colonies with a mixed ppsA sequence asan ambiguous result, and any colonies with a ppsA(mut) sequence as evidence that the ppsA(mut) isolate arose

within the host, and not on the plate.If I dont recover any ppsA(mut) populations, it may be because the ppsA mutation could arise at

frequencies too low to be represented in a single sputum sample. Alternatively, plating may impose a selection.To overcome these challenges, I could use highly sensitive methods to detect ppsA(mut) directly from sputum,

without plating. I could use real time PCR using specific molecular beacons to detect the ppsA mutationdirectly from sputum, as has been recently pioneered in the GeneXpert Mtb diagnostic

50.

Together, these studies may for the first time demonstrate that high frequency genetic variation of avirulence factor occurs in infected hosts. The proposed macrophage studies will indicate whether this variation

can functionally modulate the early host pathogen interaction.


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Aim II: To test the hypothesis that clinical strains from different lineages possess functionally different

requirements for growth in vivo.

Large sequence polymorphisms (LSPs), SNPs, and other genetic patterns define distinct lineages of the

MTC, which was once thought to be a genetically homogeneous population36

. These genetic differences havebeen associated with wide host tropism, spectrum of disease, and drug sensitivity profiles, yet the functional

consequences of genetic diversity have not been studied in a comprehensive manner at the molecular level. Ihypothesize that different clinical lineages use overlapping but distinct strategies for in vivo growth and

survival. I propose to investigate the genetic requirements for in vivo infection in different Mtb lineages tounderstand how these lineages differ in their manipulation and interaction with the host environment.

Aim IIA: To test the hypothesis that clinical strains exhibit heterogeneity in genetic requirements for

survival during infection.

I propose to test the hypothesis that clinical strains from different lineages possess different genetic

requirements for growth. I propose to test this hypothesis using TraCS, a forward genetics approach based onthe TraSH (transposon-site hybridization) method pioneered in Mtb 10 years ago

51. TraSH utilizes phage

transduction to deliver a mariner transposon which randomly integrates into TA dinucleotides across thegenome, generating an Mtb library of ~10

5transposon mutants. This library can then be subjected to a variety

of growth conditions. In TraSH, surviving mutants are used to generate DNA probes complementary to thejunction of the transposon and the chromosome, and are then hybridized to an Mtb microarray. TraCS allows

direct Illumina sequencing of the transposon-chromosome junctions, which affords greater resolution oftransposon insertion sites and a much larger dynamic range in estimating the fitness of each mutant.

TraSH has been instrumental in revealing the genetic requirements for growth in H37Rv. Specifically,214 genes were differentially required for growth in mouse spleen at varying times post infection compared to

growth on a 7H10 agar plate52,53

. Although many of the 214 genes have unknown functions, some encodeproducts which are known to vary between lineages, such as PGL. Here, I propose to conduct strain-based

TraCS to determine whether clinical strains from divergent lineages have different requirements for growth in amouse.

To conduct strain-based TraCS, I will create a transposon library of mutants for each clinical strain

included in the study (see Table 1). The methodology used to create the transposon library has been well-established. We will use existing protocols to introduce the transposon into every gene of the Mtb genome. Wewill recover surviving mutants by plating on rich media and scraping single colonies together to form at once,

the transposon library and the in vitro pool. Libraries already exist in our lab for H37Rv PDIM(+), H37RvPDIM(-), and the clinical Euro-American strain, Erdman. Upon extracting genomic DNA from this pool, we

will construct in vitro probes for Illumina sequencing. Construction of probes has been validated in our lab, andinvolves, briefly, shearing of chromosomal DNA, end repair and adapter ligation. We will PCR amplify

transposon junctions using one primer complementary to the adapter with an additional portion homologous tothe Illumina sequencing chip, and a second primer complementary to the transposon junction.

We will align our sequencing reads to the closest reference genome, which may need to be optimizeddepending on the composition of our clinical strain panel. We will filter our reads to ensure that the transposon

junction is present in each of them, and will then count the number of reads that correspond to each TA site inthe genome, obtaining the read depth. Read depth at a given TA site will correspond to the fitness of that

transposon insertion mutant in the pool. The second useful metric is percent of TA sites that have transposoninsertions for given region of the genome. This metric allows both a gene-level analysis and a sub-gene,

domain-level analysis. Using a non-parametric rank sum test, we will combine these two metrics to determinewhich genes have significantly underrepresented TA hits in the in vivo pool compared to the in vitro pool.

Genes that are underrepresented in the in vivo pool will be considered essential for growth in a mouse.I will select strains to ensure diversity across the Euro American, Indo Oceanic, West African, East

Asian, and East African/Indian lineages. Selection of strains will be subject to public availability. I will


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attempt to balance the number of sequenced, well-characterized, in vitro-passaged strains with clinical isolatesabout which relatively little is known. A candidate panel of strains is indicated in Table 1. PGL production

varies across these strains, allowing a sub-analysis (see Aim IIB) to correlate PGL production with requirementsfor growth in vitro and in vivo.

Strain name Sequenced? PGL status Lineage37

Reason for including

HN878 Yes + East Asian Reference

621 No ? East Asian Immunologic data, our lab

NHN5 Yes - East Asian No PGL

K85 Yes ? West Africa 2 Reference

668 No ? East African Indian Immunologic data, our lab

947/01 No + Indo Oceanic Transcriptome/growth54

K21 Yes + Indo Oceanic Reference

581 No - Euro American Immunologic data, our lab

Erdman Yes - Euro American Reference

Table 1. Candidate strains to be included in TraCS.

To create the in vivo pool, we will infect C57Bl/6 mice with the clinical strain libraries by high-doseaerosol. The strains tested here have emerged because of successful pathogenesis strategies in the context of the

human host. Within the context of the experimental mouse model, aerosol infection imposes selection whichmost closely recapitulates that of the human host, compared to the intravenous route. This would be the first

TraSH/TraCS experiment to use the aerosol route of infection, and may reveal important differences betweenthese two in vivo environments as they impose selection the bacterium. Aerosol doses with 20,000 CFU per

mouse have been successfully performed in our lab, thus I propose to distribute the TraSH library of 105

mutants equally across five mice per strain, in triplicate. At two and four weeks post infection, I will pool

organs from each set of 5 mice, and will treat this pool as a single sample per time point.I first propose to do TraCS on existing H37Rv PDIM(+) and PDIM(-) TraSH libraries. This experiment

will allow a comprehensive characterization of the role of PDIM in infection, and will be an essential pilot

study to validate our infection parameters. I propose to infect PDIM(+) and PDIM(-) strains by high doseaerosol. We will harvest lungs at two and four weeks post-infection to reveal relative fitness before and after

onset of the adaptive immune response. We will titer the infected organs, plate mutants, isolate genomic DNA,create TraCS probes as described above, and send probes for Illumina sequencing. These pilot experiments will

help us determine the feasibility of the aerosol route of infection, which we will assess by mouse survival,recovery of mutant pools, and complexity of mutant pools as compared to TraSH by intravenous infection.

Furthermore, this experiment will be a natural extension of Aim I discussed above, revealing genes whoseproducts compensate for PDIM loss, potentially revealing roles for PDIM during infection.

Upon successful optimization of infection parameters and analysis methods, we will repeat the TraCSexperiment using clinical strain libraries as indicated in Table 1. For each clinical strain, we will recover

surviving mutants to form an in vivo pool as described above. Upon sequencing and analysis as described

above, we will measure in vivo fitness for viable mutants at two weeks and four weeks post-infection (or asinformed by the pilot experiment) as compared to each mutants in vitro fitness. Mutations that result insignificant change in fitness in vivo compared to in vitro will be selected for further analysis.


Generation of the strain-based TraCS dataset will be a significant contribution to our knowledge ofglobal strain diversity in Mtb. Each step of the experimental process proposed above is a significant and novel

contribution to the field. The pilot experiment allows a comparison of genes required for growth of H37Rvduring aerosol infection in which the lung carries a large bacterial burden versus the previously established


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intravenous route which results in a large bacterial burden in the spleen. I predict there will be significantdifferences in the subset of required genes during the initial two weeks of infection, as aerosol infection may

select for different cell surface molecules on infecting bacilli. Furthermore, these experiments may show therole(s) for PDIM during infection, by revealing essential genes in its absence.

Our approach to find functional consequences of strain-based genetic differences would be anunparalleled characterization of strain diversity in Mtb, at the molecular level. I predict there will be a

significant core set of genes required for growth in all strains. This core set may represent a reduced subset ofthe genes required for infection as defined previously for H37Rv. Furthermore, I predict the in vitro core set

will be different from in vivo set, the latter being enriched for genes specific to intracellular survival, such asstress response, hypoxia, iron limitation, and cell wall remodeling. Finally, the pairwise comparisons of strains

within and between lineages, as well as multiple strain comparisons will reveal lineage and strain-specificessential genes (by ANOVA and the Tukey-Kramer HSD and post-hoc tests). Transcriptional profiling of

clinical strains and single-strain studies suggest that the gene expression profile for strains within a lineage aremore correlated than strains from different lineages53. For example, the mce4 locus, encoding an important

cholesterol uptake system, was upregulated, while nitrate reductase, important for in vivo survival, wasdownregulated in ancient strains compared to modern strains. Despite these transcriptional data, we may find

that the genes essential for surviving in a mouse are the same across clinical strains. This finding may indicatethat there is a highly conserved core genome across the strains tested, and that a high degree of functional

redundancy in Mtb genomes may compensate for the genetic differences between strains. This finding mayalso indicate that host variability is a more dominant driver of functional diversity in strains than bacterial

variability, a hypothesis we could test by conducting TraCS in mutant strains of mice.There are technical limitations to our experimental setup that may skew our ability to answer the

questions posed above. Some of these limitations are specific to the TraCS methodology, such as biasintroduced by type of PCR used to amplify the TraCS library. We can measure some of this bias by conducting

quality checks on our data to measure the ratio of specific to non-specific PCR products amplified. In addition,despite its advances, Illumina sequencing affords poor resolution of repetitive regions of the genome; we will

analyze our data in light of this knowledge. There are also technical challenges posed by clinical strainsthemselves, including differential ability to grow on 7H10 plates and unknown in vivo growth kinetics leading

to different courses of infection. While we will be able to gauge some of these growth differences by assessing

growth curves and comparing O.D. to CFU, using previously characterized clinical isolates will allow us todraw on published data to predict the growth of these strains. Finally, high-dose aerosol infection may present atechnical challenge, as we may find from our pilot experiments that PDIM(+) H37Rv delivered by high dose

aerosol causes excessive pathology and reduced survival for mice. In this case, we will conduct theexperiments proposed above using intravenous injection. While we will not capture important events

surrounding the initial colonization of the lung, we will still be able to identify significant differences inrequirements for intracellular growth across clinical strains.

Importantly, I recognize that this is a large undertaking, and thus propose to do the work describedabove in collaboration with a post-doctoral fellow in the Fortune Lab.

Aim IIB: To test the hypothesis that strains that produce PGL have functionally different requirements

for surface lipids during in vivo growth.The data generated from the TraCS experiment will reveal which genes are differentially required for in

vivo growth between clinical strains. I propose to investigate a portion of the TraCS data to establish thefunctional consequences of a known source of inter-strain heterogeneity, namely PGL production. Both PGL-

producing and PGL-lacking strains survive and persist globally. I hypothesize that loss of PGL production,particularly in the Euro-American strains, resulted in compensatory evolution of other surface lipids in these

strains. Indeed, there is in vitro evidence that loss of PGL-related lipids results in increased sulfolipid (SL)production

55. In vivo, PGL modulates the early host-pathogen interface. Some studies also position PGL and

other surface lipids as a physical shield from host defenses and a metabolic sink for storing carbon units during


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cholesterol metabolism55,56,57

. Strains that dont produce PGL may rely more heavily on other surface lipids toperform these actions.

To test the hypothesis that differential PGL production creates different requirements for other surfacelipids during mouse infections, I propose to cluster the strain-based TraCS data by PGL production. I will

identify genes differentially required for mouse infection in PGL(+) versus PGL(-) strains, and determinewhether surface lipids are enriched in this set, using the hypergeometric test. I will furthermore test the

functional consequences of differential PGL production by measuring in vitro production of known surfacelipids such as PDIM and SL, as well as unknown lipids. I will accomplish this by conducting mass-

spectrometry-based lipidomics for the strains included in the TraCS study, in collaboration with the laboratoryof Dr. Branch Moody, where this methodology is well-established. Recognizing that different in vitro

conditions can alter a strains lipid profile, I will grow the clinical strains, a PGL(-) version of HN878 as anisogenic control, and a PDIM(-) H37Rv mutant, to equal O.D.s in standard glycerol-containing 7H9 media, and

7H9 containing propionate, known to foster lipid production that is more reflective of growth in vivo54

. Ipredict that this analysis will reveal lipid-based differences across clinical strains. Together, the TraCS and

lipidomics data will help me assess whether PGL production is an important predictor of a clinical strains lipidprofile and its requirements for surface lipids during infection.


The experiment proposed above is a tractable way to parse trends from the large TraCS dataset, andestablish functional consequences for specific genetic requirements for growth. I predict that PGLs metabolic

and immunomodulatory niche is filled by other lipids in PGL-lacking strains, resulting in different lipid profilesacross these strains. We may be able to identify some of these lipids based on differential essentiality in TraCS

data, and a complementing approach, in vitro lipidomics. In particular, I predict that the genes contributing toPDIM production and transport will be more essential in vivo in PGL-lacking strains, and that this will

correspond to increased in vitro PDIM production. However, there may be significant strain-to-strainheterogeneity in PDIM and PGL production that does not correlate with lineage; our lab has observed similar

heterogeneity in ESX1 substrate secretion in a small panel of clinical strains. As such, I may find that PGLproduction is not a significant predictor of strain-based lipid profiles. In this case, I will look to the lipidomics

data for other candidates whose production may track with lineage, and the TraCS data for other cell wall

virulence factors (such as ESX1 substrates) that may be better predictors of strain-based lipid profiles.

Strain-based heterogeneity must be incorporated into the existing paradigm of Mtb pathogenesis. Strains

differing in their modes of virulence may require specialized treatment programs, may possess unique antigenicand drug resistance signatures, requiring more specificity in diagnostics, and may result in heterogeneous

immune outcome. Furthermore, examining the relationship between Mtb phylogeny and functional diversity inpathogenesis may shed light on the long co-evolution of Mtb and its hosts, revealing valuable information about

differing host environments. The experiments proposed here examine functional consequences of bothunstable, cell-to-cell variability and stable, lineage-driven variability, at the host-pathogen interface.


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Supplemental Information

Figure S1. The MTC phylogeny, as determined by large sequence polymorphisms, exhibits a tightgeographical structure. The Euro-American lineage diverged partially as a result of a 7bp deletion leading to

loss of PGL production37

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