SUPPLEMENTARY INFORMATION
SUPPLEMENTARY FIGURES
a b c
e f
g h
d
Supplementary Figure 1. mRNA expression of PKC isoforms in human whole
blood. mRNA expression of various isoforms of PKC in TB progressors, expressed
as log2 fold change over matched controls, from ACS group. The dotted line
represents the mean log2 fold change, nonlinear spline function in 46 progressors
and 107 healthy QuantiFERON positive controls. The blue shaded area represents
99% confidence intervals. (a) PKCα, (b) PKCβ, (c) PKC, (d) PKC, (e) PKC, (f)
PKC, (g) PKC and (h) PKC.
a b c
d
0
1.010 5
2.010 5
3.010 5
4.010 5
5.010 5
**
Lesi
on A
rea
( m
2 )
Tat Control Tat δV1.1
1.0
1.2
1.4
1.6
1.8
2.0
2.2
Lung
wei
ght i
ndex
*
Tat controlTatV1.1
Gating Strategy for myeloid cell populations in the lungs
Neutrophils
Alveolar Mphs
CD11b+ MHCII+ Mphs
CD11c+ MHCII+ DCs
Supplementary Figure 2. A peptide inhibitor of PKCδ increased inflammation
in wild-type mice. Following infection with 350 CFU of Mtb, mice were treated with
TatVδ1.1, a PKCδ-specific peptide inhibitor (3 mg/kg) for three times a week via
intraperitoneal injection for 5 weeks. Mice were then sacrificed to determine (a) lung
weight index, a proxy of inflammation, (b-c) H&E stained lung sections and
subsequent quantification of lesion area. Results are mean ± SEM of 9-10
mice/group and analysed by student t-test, * p < 0.05, ** p < 0.01 versus Tat control.
Gating strategy to define (d) alveolar macrophages, neutrophils and (e) activated
macrophages and dendritic cells in the lungs of Mtb-infected mice.
Act Mph DCs Neutrophils0
1
2
3
4
*
**%
cell
pop
ulat
ions
B cells CD4 CD80
10
20
30
40
50
**
*
% c
ell p
opul
atio
ns
B cells CD4 CD80
10
20
30
40
% c
ell p
opul
atio
ns
Act Mph DCs Neutrophils0.0
0.1
0.2
0.3
0.4
0.5
% c
ell p
opul
atio
ns
a b
c d
e f
8 w
eeks
4 w
eeks
Act Mph DCs Neutrophils0.0
0.1
0.2
0.3
0.4
0.5
*
Cell
Num
bers
(x10
6 ) *
B cells CD4 CD80
2
4
6
8
Cell N
umbe
rs (x
106 )
Act Mph DCs Neutrophils0.00
0.02
0.04
0.06
Cell
Num
bers
(x10
6 )
B cells CD4 CD80
1
2
3
4
5
6
Cell N
umbe
rs (x
106 )
g h
Lymph Nodes
Supplementary Figure 3. Immune cell populations in the lymph nodes of
PKCδ-/- mice following Mtb infection. Single cell suspension of thoracic lymph
nodes was analyzed for percentage and total cell numbers of immune cell
populations using FACS at 4 (a-d) and 8 (e-h) weeks after infection with 1000 CFU
of Mtb. Data are represented as mean ± SEM of n = 4-5 mice/group. Data is
analyzed using unpaired, student t-test, * p < 0.05, ** p < 0.01, *** p < 0.001, versus
WT control mice. Surface markers and gating strategy of different cell populations
were determined as in the main manuscript and in supplementary figure S2
respectively.C
FU /
2x10
5 BM
DM
Medium
Oleic A
cid
Nat-LDLs
Palmitic
Acid0
20000
40000
60000
80000 WT PKC -/-
* *****
Nitr
ic O
xide
( M
)
Medium
Oleic A
cid
ox-LDLs
Nat-LDLs
0
30
60
90
120
*** ****
****
*
a b
05000
1000015000200002500030000350004000045000
**ControlPKC siRNA
4hpi 48hpiC
FU /
1x10
5 BM
DM
c d
0 4 12 240.0
0.5
1.0
1.5
2.0
Prk
cd F
old
Cha
nge
*** ***
hrs post Mtb
Oil
Red
O(O
.D.4
90nm
)
Medium
Oleic A
cid
Ox-LDLs
Nat-LDLs
Palmitic
Acid0.0
0.1
0.2
0.3
0.4
****
e
Supplementary Figure 4. PKCδ expression in HN878 infection, knockdown of
PKCδ in wildtype macrophages and effect of saturated and unsaturated fatty
acid on PKCδ-/- macrophages. (a) BMDMs were infected with an HN878 strain of
Mtb to confirm CAGE expression profile (Figure. 6A) by quantitative RT-PCR. (b)
BMDMs were transfected with 25nM anti-PKCδ siRNA using Lipofectamine (2000) in
the Opti-MEM medium for 72 hours. Cells were then washed and infected with Mtb
(MOI=5) for four hours to determine growth at indicated time points. (c-e) BMDMs
from wild-type and PKCδ-/- mice were pretreated with either oleic acid, palmitic acid,
native-LDLs, ox-LDLs or left untreated overnight. Cells were then infected with Mtb-
containing medium with or without above mentioned fatty acids, native LDLs and ox-
LDLs for 3 days to determine (c) mycobacterial burden, (d) nitric oxide production
and (e) lipid accumulation by quantifying absorbance of Oil red O at 490nm. Data are
represented as mean ± SEM of (a) three and (b, c-e) two independent experiments,
Data is analyzed using unpaired, student t-test, * p < 0.05, ** p < 0.01, *** p < 0.001,
**** p < 0.0001versus WT control macrophages.
g/L
Arachidonic
acid
-Linolen
ic ac
id
Palmitic
acid
Behen
ic ac
id
Cervonic
acid (D
HA)
Timnodonic
acid (E
PA)0.00.20.40.60.81.01
2345
20
40
60 WTPKC -/-
g/L
0.00.20.40.60.81.0
123455
10152025
*
** **
a b
c d
g/L
0.00.10.20.30.4
12345
1020304050 WT 70CFU
WT 1000CFU
e
ng/m
l
IL-12p40
IFN-IL-1a IL-1b IL-6
0
1
2
35
10
15
20 WT-70 CFUWT-5000 CFU
*
*
*
*
f
Supplementary Figure 5. Serum fatty acids by metabolic analysis following
low-dose (70CFU) of Mtb infection. (a-b) PCA plots with variation in each
component are indicated in the parenthesis at 4 weeks and 8 weeks after infection
(70 CFU). (c-d) Serum levels (µg/L) of selected host-protective (arachidonic acid, α-
linolenic acid and palmitic acid) and detrimental fatty acids (behenic acid, DHA and
EPA) during Mtb infection. (e) Differences in fatty acids was not a function of
bacterial loads in WT mice, as shown at different Mtb infection dose. (f) Production
of inflammatory cytokines in the lungs of WT mice is indeed a function of bacterial
loads. Data are represented as mean ± SD of n = 5 mice/group. Data is analysed
using univariate and multivariate statistical techniques (PCA powers > 0.5, PLS-DA
VIP > 1.0, effect size > 0.8, and student t-test p < 0.05) versus WT control mice.
METHODS
RNA-Seq data from ACS cohort
Whole blood RNA-Seq data from the Adolescent Cohort Study1 was downloaded
from the Gene Expression Omnibus (Series GSE79362, primary samples) and
aligned to the hg19 human genome using gsnap2 as in the original study.1
Normalized gene-level expression estimates were derived from mapped read pairs
following the procedure implemented in.3 Briefly, mapped read pairs were assigned
to genes by collapsing all transcripts into a single gene model and counting the
number of reads that fully overlap the resulting exons using htseq (v.0.6.0)4 with
strict intersection and including strand information. Gene models for protein-coding
genes were downloaded from Ensembl (GRCh37.74). Reads that mapped to
multiple locations were only counted once and those mapping to ambiguous regions
were excluded. Log2-transformed count values normalized by adjusted library counts
were computed using the cpm function of the edgeR package.5
Metabolic analysis of serum
Chromatographic analyses of the derivatized samples were performed using a
Pegasus GC x GC-TOFMS (Leco Corporation), utilizing an Agilent 7890A GC
(Agilent) coupled to a time of flight mass spectrometer (TOFMS) (Leco Corporation)
and a Gerstel Multi-Purpose Sampler (MPS) (Gerstel GmbH & co. KG) as described
previously6, with minor modifications. This included that a split ratio of 1:2 was used,
a Restek Rxi-17 (1 m, 0.25 mm i.d., 0.25 mm d.f.) column for the second dimension
separation, and the secondary oven programmed with an offset of +15°C, increasing
at 4.5°C per min to a final temperature of 300°C. Cryogenic modulations and a hot
pulse of nitrogen gas of 0.5s, every 3s was used to control the effluent emerging
from the primary column onto the secondary column. Detection was achieved by
using MS detection in full scan mode (m/z 50-800). Leco Corporation ChromaTOF
software (v4.5) was used for peak finding and mass spectral deconvolution at an S/N
ratio of 50, with a minimum of 2 apexing peaks. Peak identification and alignment
was done as previously described.6 Prior to statistical data analysis, a standard
metabolomics data clean-up procedure was applied. All compounds were normalized
relative to the internal standard by calculating the relative concentrations of each.
Following this, variables showing no variation between the groups were removed,
and a data filter was applied to each variable to eliminate those with more than 50%
zero values in each group.7 Auto-scaling was performed across the entire dataset to
place all metabolites on equal footing.8 Various multivariate (principal components
analysis (PCA);9 partial least squares–discriminant analysis (PLS-DA)6 and
univariate (effect sizes; unpaired t-test)10 biostatistical analyses were applied using
Matlab with Statistics and PLS Toolbox Release (2012).
Treatment of mice with PKCδ-specific peptide inhibitor
Lyophilized Tat control and TatVδ1.1 peptides were reconstituted in sterile saline
solution. Aliquots of these peptides were stored at -80°C. Wild-type mice were
infected with 350 CFU of Mtb via intranasal challenge. After 2 days of infection, mice
were then injected three times a week with 3 mg/kg of TatVδ1.1 peptide inhibitor via
intraperitoneal route for 8 weeks. Mice were then sacrificed to determine parameters
for lung inflammation.
Transfection of anti-PKCδ siRNA in primary macrophages
Macrophages were transfected with complex of 25nM on-target plus (OTP) siRNA
against PKC-delta (Dharmacon) and Lipofectamine 2000 (Molecular Probes,
Invitrogen) in the Opti-MEM medium for 72 hours. Macrophages were then infected
with Mtb at MOI of 5 for four hours at 37C and bacterial growth was determined at 4
and 48 hours post-infection as described previously.11
Primers
The primer sequences were used for qRT-PCR were as follows:
Gene Name Primer Sequence
Mouse Prkcd forward 5’-TGC GCA TCT CCT TCA ATT CC-3’
Mouse Prkcd reverse 5’-AGC GCC TTC ATA GAT GTG GG-3’
Mouse Hprt forward 5’-GTT GGA TAT GCC CTT GAC-3’
Mouse Hprt reverse 5’-AGG ACT AGA ACA CCT GCT-3’
Human PRKCD forward 5’-TGT GCC GTG AAG ATG AAG GAG-3
Human PRKCD reverse 5’-TAG ATG TGG GCA TCG AAC GTC-3’
Human HPRT1 forward 5’-AGG CGA ACC TCT CGG CTT T-3’
Human HPRT1 reverse 5’-AAG ACG TTC AGT CCT GTC CAT-3’
Statistical analysis
For RNA-seq data analysis, gene-level log2 fold changes comparing each
progressor sample to the average of demographically matched control samples were
computed using the Adolescent Cohort Study metadata provided in1, and assigning
Time to Diagnosis values for each sample according to the original definitions. The
gene expression fold changes for all progressor samples were modeled as a
nonlinear function of Time to Diagnosis for the entire population using the smooth
spline function in R with three degrees of freedom. Ninety-nine percent confidence
intervals for the temporal trends were computed by performing 2000 iterations of
spline fitting after bootstrap resampling from the full dataset.
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