Primary HIV-1 infection was associated with differential T-cell and NK-cell activation.

<p>T-cells (A) and NK cells (B) were activated following HIV infection. The proportion of activated T-cells and NK cells was significantly correlated before (C), but not following HIV-1 infection (D). In contrast, during primary infection, both T-cell (E) and NK-cell (F) activation were positively correlated with HIV viral load (log₁₀ copies/ml) but not CD4+ T-cell counts (G and H respectively).</p

Early after HIV infection blood NK cells increased their expression of lymphoid, but not gut homing receptors.

<p>The proportion of NK cells expressing CCR7 was increased following HIV infection (A). The proportion of NK cells expressing CCR7 was reduced during later stages of primary infection (B). The proportion of NK cells that expressed α4β7, was not changed during primary HIV-1 infection (C).</p

Following infection, an increased proportion of activated NK cells in blood expressed Killer Immunoglobulin-like Receptors (KIR).

<p>The proportion of KIR_pos NK cells in blood increased following HIV infection (A). The proportion of KIR_pos NK cells did not differ between stages of primary HIV infection (seronegative, sero-discordant or seropositive stages) (B). KIR_pos NK cells were more activated than KIR_neg NK cells (C).</p

Following HIV infection NK-cell responses to stimulation were diminished.

<p>Natural Killer cell degranulation (A) and IFN-γ secretion (B) responses after stimulation with IL-2 alone or with IL-2 and 721 cells (adjusted for background) or with PMA/Ionomycin. Data are adjusted for background responses to media alone.</p

Validation of proteomic analysis by western blot using a monoclonal antibody to CAECAM 18.

<p>CAECAM was uniquely detected in all 3 independent experiments.</p

Network analyses of protein clearance markers.

<p>The majority of clearance markers belong to one of four networks. Network A centers on CD44 and CCND1 and consists of genes involved in the cell cycle and RNA post-transcriptional modification (<b>Figure A</b>). Network B centers on IFNβ1, NF-κB, ERK1 and MAPK and includes several additional genes involved in antimicrobial responses, such as TLR8 (<b>Figure B</b>). Network C centers on TP53, and TGF-β and is associated with the cell cycle and with proliferation (<b>Figure C</b>). Network D centers on CCL2, CCL4 and IFN-γ, which are associated with cell activation and migration and which play a central role in tuberculosis (<b>Figure D</b>). Solid lines denote a direct protein-protein interaction, such as binding; dotted lines denote other relationships, such as co-expression, regulation and activation, phosphorylation or cleavage relationships. The intensity of protein expression is denoted in shades of red proportionate to the level of expression.</p

Classification of peptides according to qualitative or quantitative differences between activated THP-1 cells infected with <i>M</i>. <i>tuberculosis</i> H37Rv or left uninfected and sampled at Day 1 or Day 5.

<p>Classification of peptides according to qualitative or quantitative differences between activated THP-1 cells infected with <i>M</i>. <i>tuberculosis</i> H37Rv or left uninfected and sampled at Day 1 or Day 5.</p

General work flow.

<p>THP-1 cells were activated using 50 nM PMA and infected with <i>Mtb</i> H37Rv. The infected macrophages were treated with 3 μg of INH and 9 μg of RIF for 1 day (Day 1 Infected) (<i>Mtb</i> remaining in the cells, <i>i</i>.<i>e</i>., infection stage) and 5 days (Day 5 Infected) (no <i>Mtb</i> remaining in the cells 2 days after clearance, <i>i</i>.<i>e</i>., clearance stage). <i>Mtb</i> clearance was confirmed by CFU determination at 3 days post-infection. THP-1 cells treated with drugs (without <i>Mtb</i>) for 1 day (Day 1 Uninfected) and 5 days (Day 5 Uninfected) post-infection were used as background controls. The culture supernatant and cell lysates were collected. CFU counts were performed to confirm the clearance stage of <i>Mtb</i> in intracellular and extracellular compartments from all experiments. Three biological replicates of the experiments were performed. The proteomes were analyzed by GeLC MS/MS. A western blot was performed to validate the proteins identified by GeLC MS/MS. The candidate clearance markers were compared to markers from patients treated with anti-TB therapy from previous studies.</p

Comparison between ML ratios measured at different cross-sectional surveys.

<p>Spearman rank correlation coefficient is used to assess the relationship between ML ratios across different surveys according to parasite positive/negative status at the time the ML ratio was measured. Results are shown for children that were parasite positive at the May 2007 or 2008 or 2009 or 2010 survey and parasite positive (A) or parasite negative (B) in subsequent surveys (that is 2008–2011). In (C) and (D) results are shown for children that were parasite negative at the May 2007 or 2008 or 2009 or 2010 survey and parasite positive (C) or parasite negative (D) in subsequent surveys (that is 2008–2011). Rho values from all comparisons are shown and statistically significant comparisons (P<0.05) indicated in shaded boxes. Unshaded boxes represent comparisons that showed no significant correlation.</p

ML ratio positively correlates with risk of clinical malaria.

<p>Kaplan-Meier plots of the relationship between ML ratio and time to first episode of clinical malaria during follow-up is shown. (A) and (B) represent results using ML ratios measured in the May 2008 baseline survey and consider a follow-up period ending on 31^st December 2011. However, most parasite positive children had experienced their first clinical malaria episode within a year since sampling in the May 2008 baseline survey and so the plots show data for the first 12 months of follow-up. (C) and (D) represent results based on ML ratios measured at each of five surveys (May 2007, 2008, 2009, 2010 and 2011) and consider time to the first episode within the respective one year inter-survey periods as the primary endpoint. The hazard ratios (HR) from unadjusted Cox regression models using ML ratio as the only explanatory variable are shown. The cumulative proportion of children with malaria in relation to their ML ratio, stratified into three arbitrary groups, is shown. “High ML ratio” and “Low ML ratio” represent children whose ML ratio falls in the top and bottom 25^th percentile of the sampled population, respectively, whilst “Medium ML ratio” represents all other children.</p