Harvard College ‘09, firstname.lastname@example.org
Human Immunodeficiency Virus 1 (HIV-1) infects several cell types, including CD4+ T-cells and monocytes. These cells are important virus targets because they are substantially depleted during infection and can also store dormant virus, which fuels the disease at later stages. A crucial part of the recognition and clearance of infected cells involves their presentation of pieces of viral protein, called epitopes, at their surface for recognition by immune cells. These pieces originate from the degradation of viral peptides by intracellular peptidases. It is unknown if cell types that are infected by HIV present similar viral peptides to evoke analogous immune responses. Here, I examined whether CD4+ T-cells and monocytes would exhibit differences in the production of viral peptides and if that could affect the HIV-specific immune responses. I measured the activity and kinetics of intracellular peptidases involved in viral peptide degradation in CD4+ T-cells and monocytes using an in vitro fluorescence-based hydrolytic activity assay. Peptidases’ activities were higher and faster in monocytes. Speculating that these differences in activity might affect viral peptide production, I measured the production of a 9-amino acid long HIV epitope (RK9) from a longer peptide using an intracellular degradation assay where the longer peptide is incubated with cytosolic extract from CD4+ T-cells and monocytes. I used RP-HPLC to identify degradation products. The longer peptide’s degradation and RK9 production were faster in monocytes. In addition, monocyte degradation products elicited greater epitope-specific immune responses. I sought to determine the overall effect of these cell-type-specific differences in peptidase activity on the intracellular degradation of randomly selected HIV epitopes using a similar intracellular degradation assay. The epitopes’ half-lives were significantly shorter in monocytes than in CD4+ T-cells. These differences in antigen processing could affect presentation of HIV-1 peptides to immune cells and might influence clearance of infected CD4+ T-cells and monocytes.
Human Immunodeficiency Virus 1 (HIV-1) is a retrovirus that preferentially infects human nucleated cells with tropism towards CD4+ T or monocyte-derived cell lines (Callaway et al., 1999; Sup. Fig. A). After infection, the virus integrates its retro-transcribed DNA into the host cell’s genome and can either reproduce numerous active progeny or remain quiescent (Stevenson et al., 1990; Hauber et al., 1987). Cells with dormant virus, known as viral reservoirs, can linger in the host indefinitely (Chun and Fauci, 1999).
Of all the Peripheral Blood Mononuclear Cell (PBMC) subsets, researchers implicated CD4+ T-cells and monocytes as key reservoirs of virus during infection (Smith et al., 2003; Brenchley et al., 2004). Severe depletion of CD4+ T-cells occurs during the acute stage of HIV infection with around 40-50% lymphocyte loss (Hel et al., 2006; Sup. Fig. B). However, monocytes do not drastically decline in numbers as CD4+ T-lymphocytes do (Douek, 2007). During chronic infection, viral levels in the blood rise, the immune system weakens, and consequently AIDS symptoms, like Kaposi’s sarcoma, occur (Douek, 2007). Progression to AIDS is faster among HIV-infected patients with drawn out symptoms during acute infection due to a weaker immune response (Pedersen et al., 1989).
Immune response to HIV-1 infection
Effective regulation of the acute and chronic phase by the host’s immune system gives the patient a much better prognosis for the disease (Vanhems and Beaulieu, 1997). Patients with little loss of CD4+ T-cells have slow disease progression, hence validating the importance of an efficient immune response (Sheppard et al., 1993). For this study, I was interested in aspects of the cell-mediated immune response because cell types involved in this response that undertake anti-HIV specific functions could increase the host’s capability to control HIV-infection (Schmitz et al., 2001).
To initiate cell-mediated responses, circulating dendritic cells (DCs) in the bloodstream can phagocytose the foreign pathogens, process them, and present the viral peptides to immature CD4+ and CD8+ T-cells in order to begin their maturation process (Randolph et al., 2008). Mature CD8+ T-cells become cytotoxic T-lymphocytes (CTLs) that target and kill infected cells or produce other antiviral responses, and mature CD4+ T helper cells either assist in the recruitment of CTLs, aid in the production of neutralizing antibodies, or produce cytokines that enhance the immune response (Betts et al., 2001; Hel et al., 2006). Thus, infection of CD4+ T-cells greatly weakens the immune system. Also, it gives a rationale for the success of long-term non-progressors who have highly efficacious T-cell-mediated responses against HIV (Paroli et al., 2001).
One of the most important events in a cell-mediated immune response is antigen processing, which encompasses the degradation, transport, and presentation of both self and foreign proteins to CTLs. There are two major antigen processing pathways: endogenous and exogenous.
The endogenous pathway
Defective, newly synthesized, or intracellular proteins can be tagged with multiple copies of ubiquitin, a protein that targets them for proteolysis in the proteasome (Schubert et al., 2000). Three of the proteasome’s beta subunits bear the protease’s active sites: caspase-like, chymotrypsin-like, and trypsin-like, which cleave proteins after acidic, hydrophobic, and basic amino acids, respectively (Kopp et al., 1997).
After proteasomal processing, the endogenous pathway utilizes peptidases in the cytosol, such as tripeptidyl peptidase II (TPPII), thimet oligoendopeptidase (TOP), and aminopeptidases, to further degrade the peptide products (Groothuis et al., 2005). Recently, an in vitro intracellular experiment demonstrated that TPPII was necessary for the generation of an HIV-1 epitope presented through the endogenous pathway in DCs (Seifert et al., 2003). Like TPPII, TOP can generate C-termini that are different from peptides produced by the proteasome, but TOP prefers to cleave shorter peptides (Oliveira et al., 2001). Saric and colleagues determined that TOP was a critical intracellular endopeptidase that degraded proteasomal products of 9-17 amino acids (Saric et al., 2004). The large family of cytosolic aminopeptidases further processes these peptide products (Hattori and Tsujimoto, 2004). However, an experiment on the capability of mice lacking leucine aminopeptidase to present viral peptides found that that this peptidase was not necessary to generate peptides for the MHC-I pathway (Towne et al., 2005). This evidence does not contradict previous findings that implicate aminopeptidases as contributors to antigen processing. Rather, it suggests that many peptidases contribute to the processing of viral peptides.
After cytosolic processing, peptides around 9 amino acids long with a preferred C-terminal residue can be transported to the endoplasmic reticulum (ER) by transporter associated with antigen processing (TAP) (Larsen et al., 2005). In the ER, degradation by ERAP-1 may continue before the peptide is loaded onto an MHC class I molecule. Stronger binding affinity of the peptide in the MHC binding groove displaces the complex from Tapasin, a protein that anchors MHC-I in the ER. Released, MHC-I/peptide travels through the trans-golgi pathway to be presented at the surface of the cell (Groothuis et al., 2005). CTLs primed by DCs recognize the epitope presented as well as the MHC-I molecule (Frahm et al., 2007; Fig. 1). If the CTL can recognize a non-self peptide, like an HIV-1 epitope, it kills the infected cell or produces other antiviral responses.
The exogenous pathway
Professional antigen presenting cells (APCs), like DCs, use this pathway during the initial activation of the immune response to extracellular pathogens. Here, MHC class II molecules on APCs present HIV-1 epitopes to CD4+ T-cells after cathepsins peptidase degradation (Pajot et al., 2007).
Antigen processing in PBMC subsets
It is generally assumed that antigen processing across cell types is similar, though one can deduce that differences in presented peptides can lead to variations in the immune response. For example, cellular production of more antigenic peptides should lead to stronger immune responses. Despite the multi-layered comprehension of antigen processing, the process is not well understood in PBMC subsets infected by HIV. Since HIV-1 targets certain PBMC cell subsets, such as CD4+ T-lymphocytes and monocytes, it is necessary to understand both the type of immune response that these subsets elicit and the processes by which they are generated.
Although direct comparisons of antigen processing in CD4+ T-cells and monocytes have not been previously reported, some work has been published about differences in antigen processing between other cell types. First, mouse fibroblast and dendritic cell lines infected with lymphocytic choriomeningtitis virus (LCMV) presented different viral peptides to CTLs, resulting in non-identical cell type-specific immune responses (Butz and Bevan, 1998). The presented peptides varied in their antigenicity; thus immune responses to DCs and fibroblasts varied, showing the possibility of functional differences in antigen processing among cell types. Another study in mice found that dendritic and non-dendritic cells presented different epitopes to CTLs during primary and secondary infection of influenza (Crowe et al., 2003). This differential presentation affected the immune response through the generation of distinct memory CD8+ T-cells for each cell type. Both of the aforementioned suggest cell-type-specific variations in antigen processing that affect the immune response, making this of high interest to HIV research since HIV-1 has tropism for select cell types.
When this is applied to immune responses to HIV-1, where infection is subset-specific and depletion of lymphocytes is skewed towards CD4+ T-cells, one realizes the need to identify and describe such differences. Furthermore, this question about the differences in antigen processing between the leukocyte subsets is applicable to vaccine research because of the increasing promise of T-cell vaccines (Johnston and Fauci, 2007). These vaccines attempt to elicit memory T-cell responses, especially CD8+ T-cell responses to kill infected cells as soon as a recipient of the virus is infected with the virus. Also, T-cell vaccines that focus on improving the host’s immune response during acute and/or chronic infection would advance the epidemiological control of the HIV pandemic. A necessary prelude to creating such a vaccine is ensuring that infected cells, CD4+ T-cells and monocytes, will present peptides that CTLs can recognize.
A thorough understanding of HIV pathogenesis will require knowledge about the immune response that HIV-infectible cells, such as CD4+ T-cells and monocytes, can elicit. First, the two tropisms of HIV-1 strains are for CD4+ T-cell lines and for monocyte lineage cells (Weiss, 2008). For instance, CD16+ monocytes, which are precursors to DCs and macrophages, have increased permissivity to HIV-1 infection with HIV-seropositive individuals having elevated numbers of this cell population (Ellery et al., 2007). Besides infectibility, both CD4+ T-cells and monocyte cell lines can serve as latent reservoirs of HIV. The creation of reservoirs begins during primary infection in resting CD4+ T-cells, and there is evidence that monocyte-derived macrophages in the gastrointestinal tract can serve as reservoirs for the virus during chronic infection (Chun et al., 1998; Smith et al., 2003).
As important as CD4+ T-cells and monocytes are during HIV infection, there is little known about the differences in their antigen processing that can potentially affect the immune response. It is known that gene expression between the PBMCs and T-lymphocyte subsets varies in HIV-seropositive individuals (McLaren et al., 2004). This introduces the possibility of variations in the expression of antigen processing genes and in the activity of their protein products, leading to functional differences. Such variations between subsets targeted by the virus may affect the kinetics, the amount, or the identity of peptides presented to CTLs and may alter the capacity of CTLs to kill infected CD4+ T-cells or monocytes.
Materials and Methods
Blood was drawn from healthy and HIV-infected donors (Massachusetts General Hospital, Boston, MA). PBMCs were isolated from blood using Ficoll-Hypaque (Sigma, St. Louis, MO) density gradient separation after a 30-minute spin at 1500rpm at room temperature. Cells were washed three times in Hank’s Balanced Salt Solution with 10mM HEPES and 1% PSG. After each wash, cells were spun at 1500rpm for 10 minutes at room temperature.
Isolation of PBMC subsets
CD4+ T-lymphocytes and monocytes subsets were isolated from PBMC and magnetically immunosorted using the EasySep® Human CD4+ T-cell Enrichment Kit and EasySep® Monocyte Enrichment Kit according to the manufacturer’s instructions (StemCell, Vancouver, Canada). Enriched cells were washed three times in Dulbecco’s Phosphate Buffered Saline (Sigma) at 1500rpm for 10 minutes at room temperature. Dry cell pellets were kept at –80°C until cytosol extraction.
Cell pellets were resuspended in a Digitonin buffer (50mM HEPES, 50mM KAc, 5mM MgCl2, 1mM DTT, 10% glycerol, 1mM ATP, 0.5mM EDTA, 0.0125% Digitonin) for 5 minutes to allow sufficient detergent permeabilization and then spun at 20,000rcf for 15 minutes. Protein concentration in both methods was measured with the DC Protein Assay Kit from Bio Rad Laboratories (Hercules, CA). Whole cell extracts were kept at –80˚C until use.
To standardize the amount of extract used in experiments, actin in each sample was compared to a sample known to contain 3µg cytosol. The normalized amount of each sample was the volume of cell extract with an actin content that matched 3µg of actin in the known sample after Western Blot analysis.
Western Blot Analysis
All samples were subjected to Western Blot analysis. First, whole cell extracts were mixed with laemmli buffer and heated at 85°C for 10 minutes. Lysates were run through a 12% SDS-PAGE gel at 100V through stacking gel and 125V through running gel. The gel was transferred to PVDF transfer membrane (GE healthcare) at 25mA overnight. The membrane was blocked at room temperature for at least 30 minutes in 5% milk and 1% NP40. Primary anti-beta actin antibody (Abcam, Cambridge, MA) was diluted to 1/20,000 in 5% milk and 0.1% NP40, added to the membrane, and incubated for 1 hour at room temperature. Following the primary, the membrane was washed 5 times with 0.1% NP40 every 10 minutes. Secondary anti-mouse and anti-rabbit antibody coupled to horseradish peroxidase (GE Healthcare) was diluted to 1/6000 in 5% milk and 0.1% NP40 and incubated for 40-45 minutes in room temperature with the membrane. Membrane was washed again as stated above. For imaging, ECL Plus Western Blotting Detection Reagents and Amersham Hyperfilm ECL were used following manufacturer’s instructions (GE Healthcare).
Peptidase inhibitors and substrates
Caspase-like, chymotrypsin-like, and trypsin-like active sites of the proteasome were inhibited by the proteasome inhibitor MG132 using a 50mM stock solution in DMSO (Sigma-Aldrich, St. Louis, MO). Aminopeptidase activity was inhibited by Bestatin hydrochloride using a 12mM stock solution in DMSO (Sigma-Aldrich, St. Louis, MO). Cpp-AAF-pAb, the TOP inhibitor, was diluted in DMSO for 1mM stock solution (Bachem, Torrance, CA). The TPPII inhibitor Butabindide oxalate was diluted with DMSO for a 10mM stock solution (TOCRIS Bioscience, Northpoint, UK). TPPII, chymotrypsin-like, and trypsin-like activities were measured with H-Ala-Ala-Phe-Amc, Suc-LLVY-Amc, and Boc-LRR-Amc [where Amc represents 7-amido-4-methyl-coumarin] respectively and were resuspended in DMSO to produce stock solutions of 10mM, 50mM, and 100mM (Biochem Bioscience, Torrance, CA). The aminopeptidase substrate Leu-AMC and the proteasome caspase substrate ZLLE-Amc were obtained from Calbiochem (San Diego, CA) to be resuspended in DMSO for respective stock solutions of 50mM and 50mM. The TOP substrate Mcc-PLGPK-Dnp was resuspended in DMSO for a stock solution of 10mM. All inhibitors and substrates were stored at –20°C.
Antigen Processing Peptidase Activity Assays
For proteasome peptidase activities, 3µg of whole cell extract was incubated in a buffer containing 20mM HEPES, 50mM KAc, 5mM MgCl2, 1mM DTT, and 1mM ATP at room temperature for 30 minutes. To ensure the specificity of the assay, 1μM MG132 was incubated with the extract. After the incubation, substrates for the different proteasome active sites were added and fluorescence was immediately measured every 5 minutes over 1 hour at 37°C with a Victor-3 plate reader (Perkin Elmer, Boston, MA). 75μM ZLLE-Amc was used to measure caspase-like activity, 100μM Suc-LLVY-Amc was used for chymotrypsin-like activity, and 25μM Boc-LRR-Amc was used for trypsin-like active site activity. Aminopeptidase, TPPII, and TOP fluorescence assays were conducted in similar conditions, excluding DTT and ATP in the buffer. Specificity was determined by incubation with 120μM Bestatin, 10μM Cpp-AAF-pAb, or 1μM Butabindide for aminopeptidase, TOP, and TPPII activities respectively. Following the 30 minute incubation, respective substrates were added: 50μM Leu-Amc, 20μM Mcc-PLGPK-Dnp, and 100μM H-Ala-Ala-Phe-Amc for aminopeptidase, TOP, and TPPII. Fluorescence was read as stated above. Excitation and emission wavelengths for trypsin-like, aminopeptidase, and TOP fluorescent products were 345nm and 405nm, respectively. Those wavelengths for caspase-like, chymotrypsin-like, and TPPII fluorescent products were 380nm and 460nm, respectively.
Synthetic peptides and RP-HPLC peptide analysis
Peptides were made by the AAPPPTEC Apex 396 multiple peptide synthesizer at the MGH peptide core (Massachusetts General Hospital, Boston MA). Peptides were then purified by reverse-phase high pressure liquid chromatography (RP-HPLC) and their sequences were verified by mass spectrometry, which showed greater than 95% purity (Partners Proteomics, Cambridge, MA). Eluted peptides produced defined peaks on the RP-HPLC where the area under the peak was proportional to the amount of peptide in the analyzed sample. Defined peptide peaks were calibrated using a 4.6x50mm 3mm C18 column (Waters, Milford, MA).
Intracellular peptide degradation assays
PBMC, CD4+ T, and monocyte whole cell extracts from the same healthy donor were resuspended in buffer containing 20mM HEPES, 137mM KAc, 1mM MgCl2, and 1mM ATP in nano-pure H2O. Subsequently, peptide was added to the solution, at which point the incubation at 37°C began. At 0, 10, and 30 minutes, aliquots of the reaction containing 30μg whole cell extracts and 6nmol peptide were stopped with 0.3% Trifluoroacetic acid (TFA) (Sigma Aldrich, St. Louis, MO). 6nmol of pure peptide in same buffer and TFA conditions was used to identify the elution time and amount of the undigested peptide through RP-HPLC. All peptides were eluted using a gradient solution of two buffers. The first was 0.05% TFA in nanopure H2O, while the second buffer varied depending on the peptide. For peptides 5RK3, A3-RK9, B57-YY9, Cw3-RL9, A33-ER8, B7-HA9, A3-K11, the second buffer either contained 50% acetonitrile (AcN) and 0.01% TFA or 100% methanol and 0.05% TFA. The second elution buffer for B7-FL9, B7-TL10, and B7-FR10 contained either 100% AcN and 0.03% TFA or 100% methanol and 0.05% TFA. Integrated peaks from digested peptides were compared to the undigested peptide. Identities of some digested peaks were verified by mass spectrometry.
Antigenicity of produced peptides
Peptide products from the 17-mer (gag) intracellular degradation assay were purified with 10% trichloroacetic acid precipitation and diluted in RPMI-1640 without serum. Subsequently, pH was adjusted to 7.4. HLA-A3+ B cells derived from HeLa cells served as target cells and were labeled with 51Cr and pulsed with 0.5ug/ml of the peptide products for 30 minutes at 37°C without serum. CTL clones specific for RK9 were incubated for 4 hours with the B cells at a 4:1 effector-target ratio. Cell lysis was defined as [(51Cr release from B cells incubated with digested products – spontaneous release) / (total release – spontaneous release)]. Cell lysis values were also compared to that of HLA-A3+ B cells that were incubated with undigested 17-mer peptide at a 0.5µg/ml concentration.
Maximum slope in the fluorescence assays was calculated with Workout 2.0 using a liner kinetic fit of the fluorescence at different time intervals (Perkin Elmer, Boston, MA). The differences in maximum slopes and fluorescence levels between CD4+ T-lymphocytes and monocytes were analyzed using the non-parametric Mann-Whitney test with GraphPad Prism 5 (La Jolla, CA). This test was used since it does not assume a normal distribution for the data, yet it assumes that both samples are independent. The half-lives of peptides used were determined using a non-linear regression of an one phase exponential decay with the same software.
Fluorogenic assays of antigen processing associated peptidases
As protein degradation of HIV-1 proteins is crucial to the eventual presentation of viral peptides to CTLs, I sought to characterize the activity of peptidases associated with antigen processing—proteasome-caspase-like, -chymotrypsin-like, and -trypsin-like; aminopeptidase; TOP; and TPPII—in CD4+ T-cells and monocytes. To do this, I utilized an in vitro fluorescence-based hydrolytic assay already developed by others in the group. Here, I incubated cytosolic extracts from peripheral blood mononuclear cells (PBMC), CD4+ T-cells, and monocytes with substrates that fluoresce upon cleavage by specific peptidases (Figure 2). The measured fluorescence of the sample was directly proportional to the activity of the peptidase over time. After a 30-minute pre-incubation with peptidase-specific inhibitors, I found that the average specificity of substrates to their respective peptidases in PBMC, CD4+ T, and monocyte subsets was between 80% and 95% (Sup. Table 1). I must note that cleavage of Leu-Amc in monocytes was about 50% specific to aminopeptidases, which indicated that another peptidase along with aminopeptidase cleaved Leu-Amc. However, fluorescence from cleavage in monocytes was much greater than in other cell extracts that we considered the discrepancies to be negligible (data not shown).
CD4+ T-cells and monocytes have different peptidase activity levels
After ensuring the specificity of the fluorescence assay, I sought to characterize the activity of the proteasome, aminopeptidase, TOP, and TPPII in two PBMC subsets targeted by HIV: CD4+ T-cells and monocytes. Prior research indicated that activities of the antigen processing machinery varied according to cell type (Butz and Bevan, 1998). Therefore, we hypothesized that the proteasome, aminopeptidase, TOP, and TPPII would show differences in activity between CD4+ T-cells and monocytes. Using blood from 8 healthy donors, I purified the PBMC and immunosorted CD4+ T-cells and monocytes. With equal amounts of cell extracts normalized through western blots against actin and GAPDH, I compared peptidase activities of whole PBMC, CD4+ T-cells, and monocytes by using the fluorescence emitted by the sample at 1 hour after the 37°C incubation. All assays were repeated three times, and the arithmetic mean of all trials was used as the final value for comparison. A representative sample shows the typical fluorescence pattern with monocytes having a higher fluorescence at the 1-hour time point than CD4+ T-cells (Figure 2). Overall, I found that proteasome-caspase-like, proteasome-trypsin-like, aminopeptidase, TOP, and TPPII had significantly higher activity in monocytes than in CD4+ T-cells (Figure 3). In proteasome-chymotrypsin-like activities, I did not find a significant difference, though other members of my lab performed a similar assay with 14 samples and found significant differences between CD4+ T-cells and monocytes (data not shown).
Faster kinetics in peptidases associated with antigen processing in monocytes
In addition to the overall level of peptidase activity, the speed at which the peptidase cleaves proteins is crucial during viral infection since the virus depends on the slow recognition of an infected cell for its replication. We investigated whether observed variations in peptidase activity levels between CD4+ T-cells and monocytes occurred with kinetic differences. Therefore, I measured the kinetics of peptidase degradation for the proteasome’s caspase-like, chymotrypsin-like, and trypsin-like active sites and for aminopeptidase, TOP, and TPPII. Like in the previous experiment, whole cell extracts of PBMC and immunosorted CD4+ T-cell and monocytes from 8 healthy donors were incubated with fluorogenic substrates for 1 hour. A representative sample shows the observed differences in slope over time (Figure 2). I used the average of the maximum slope from 3 experiments as my comparison values. Peptidases in monocytes have significantly faster degradation capacities than those in CD4+ T-cells (Figure 4). Again, the proteasome chymotrypsin-like subunit failed to show a statistically significant difference between the two subsets, but other members of the group observed differences when using a larger sample size (data not shown). These differences in antigen processing activities and kinetics imply that monocytes are capable of processing intracellular peptides to a greater extent than CD4+ T-cells, which may have downstream effects on the recognition of the infected cell.
Intracellular degradation of longer peptides to produce optimal HIV-1 epitopes
After ascertaining significant differences in activity and kinetics of antigen processing peptidases between CD4+ T-cells and monocytes, we wanted to know if those differences affect HIV-1 epitope processing. To do this, I utilized an in vitro intracellular degradation system where I incubated whole cell extracts of PBMC, immunosorted CD4+ T-cells, or monocytes with a 17-amino-acid sequence from HIV-1 Gag p17 (RWEKIRLRPGGKKKYKL, aa 15-31), called 5RK3 in this experiment, for 0 to 120 minutes (Figure 5a). When intracellular peptidases degrade 5RK3, one can visualize the peptide products through RP-HPLC where one peak indicates a distinct peptide, and the integral below the peak is proportional to the amount of peptide. At different time points during the incubation, one can examine the quantity of 5RK3 and its degradation products. Members of the lab previously showed this system to be a good approximation of the endogenous processing of intracellular proteins (Le Gall et al., 2007).
Faster degradation of 5RK3 fragment and sustained levels of RK9, a peptide product, in monocytes
Using the in vitro intracellular degradation assay described in the earlier section, I measured the degradation of 5RK3 and the generation of its optimal epitope RK9 (RLRPGGKKK), which requires a multistep cleavage of the 5 N-terminal residues (RWEKI) and 3 C-terminal residues (YKL) of 5RK3.
I studied the production of RK9 since it is associated with delayed progression to AIDS through HLA-A3 restricted CTL responses in some HIV-infected patients (Altfeld et al., 2006). Furthermore, I could infer that the observed differences in peptidase activity and kinetics affected 5RK3 degradation since this process requires the proteasome, aminopeptidases, and TPPII (Le Gall et al., 2007). I found that the degradation of 5RK3 was faster in monocytes than in CD4+ T-cells, and monocytes degraded all the 5RK3 by the second minute of the incubation (Figure 6a). As for the production of RK9, monocytes not only produced it with faster kinetics, but they also maintained higher levels of RK9 over time compared to CD4+ T-cells (Figure 6b).
Antigenic degradation products from monocytes
Besides the degradation of the original peptide, and the production of RK9, we wanted to understand the differences among peptide products of the 2 PBMC subsets. No study had shown the potential differences in peptide products from the antigen processing machinery. Such a difference could eventually affect CTL recognition of the infected cell subset. The design of the intracellular degradation assay allowed me to attribute any observed differences in the products between CD4+ T-cells and monocytes to the antigen processing machinery. We sent the products for mass spectrometry analysis to identify the peptide sequences. I found that peptides produced from 5RK3 in monocytes were shorter than those from CD4+ T-cells, presumably allowing for a better fit on MHC-I (Figure 7). In addition, monocyte degradation products contained already identified optimal HIV-1 epitopes: HLA-B27 restricted IK9 (IRLPPGGKK) and HLA-A3 restricted KK9 (KIRLPPGGK), RK9 (RLRPGGKKK), and RY10 (RLRPGGKKKY) (Korber et al., 2008; Fig. 7). This alluded to differences in the CTL recognition and antigenicity of peptide products between the two subsets. My supervisor performed a 51Cr release assay on the peptide products from the 5RK3 intracellular degradation assay to determine their antigenicity. She purified the small peptides from the incubated extracts and pulsed identical HLA-matched B cells with them (Figure 5b). Those B cells served as the targets in the release assay, and CTLs specific for HLA-A3 RK9 were the effectors. We found that products from monocytes were more antigenic than those of CD4+ T-cells (Figure 8). In fact, we achieved a maximum specific lysis of 51% from monocyte degradation products whereas those from CD4+ T-cells could not generate greater than 3% lysis. Other members of the lab observed similar results after replicating the assay twice (data not shown).
Intracellular stability of HIV-1 epitopes
The previous experiments showed how differences in peptidase activity between CD4+ T-cells and monocytes could affect the production of HIV-1 epitopes. However, antigen processing is a balance between both the production and degradation of epitopes since intracellular peptidases are constitutively active. Differences in the degradation of optimal epitopes between CD4+ T-cells and monocytes could affect epitope presentation and CTL recognition of optimal epitopes. Little is known about the intracellular stability of short peptides, though aminopeptidases have been implicated in regularly cleaving short and intermediate peptides in the cytosol (Smyth and O’Cuinn, 1994). Thus, I examined whether the degradation of HIV-epitopes, which are short peptides, was the same in both CD4+ T-cells and monocytes. Other members of my lab had characterized the half-lives of many of the known HIV epitopes in PBMC and found that although some epitopes were highly unstable, others resisted degradation over time (data not shown). We speculated that HIV epitopes that were stable in PBMC could have varying levels of stability between CD4+ T-cells and monocytes since we already saw cell type differences in epitope production. To measure the intracellular half-life of optimal HIV epitopes, I incubated them with whole cell extracts of PBMC, immunosorted CD4+ T-cells, or monocytes over 30 minutes (Figure 9). At 2, 10, and 30 minutes, I eluted the extract-peptide mixture through RP-HPLC. Like the prior RP-HPLC assay, each peptide produced a unique peak where its integral reflects the amount of peptide present in the eluate allowing me to quantify the percent left of the original epitope. By examining the proportion of peptide remaining at each time-point, I could map the epitope degradation to a non-linear regression of exponential decay that provided me with parameters to calculate the half-life of each epitope. From the existing data in the group, I chose epitopes that were relatively stable overtime in PBMC, in order to measure any notable deviations in stability in CD4+ T-cells and monocytes. Using 8 epitopes (B57-YY9, Cw3-RL9, A33-ER8, B7-HA9, A3-KK11, B7-FL9, B7-TL10, and B7-FR10), I found that the half-life of those optimal HIV-1 epitopes was significantly shorter in monocytes than in CD4+ T-cells (Figure 10). To ensure that my results were replicable, I repeated each degradation assay 3 times and averaged all values.
Different intracellular degradation of HIV-1 optimal epitopes in CD4+ T-cells and monocytes
The calculated differences in half-life of HIV optimal epitopes were not the only interesting findings revealed in our comparison of intracellular epitope degradation between CD4+ T-cells and monocytes. Apart from understanding the overall pattern of optimal epitope degradation in PBMC, we wanted to know if the kinetics of degradation was similar across the PBMC subsets that HIV-1 infects. In the same assay as above, I qualitatively assessed the kinetics of degradation for each optimal epitope (B57-YY9, Cw3-RL9, A33-ER8, B7-HA9, A3-KK11, B7-FL9, B7-TL10, and B7-FR10) between the subsets. I found that there was no consistent pattern of degradation among epitopes in both cell subsets, and these differences existed regardless of the epitopes protein’s origin (Figure 11). This suggested functional variations in subset-specific degradation of HIV epitopes.
HIV-1 infection depends on CD4+ T-cells and monocytes as vessels for proliferation and latency (Pierson et al., 2000). CD4+ T-cells undergo a more severe depletion than monocytes during infection (Pedersen et al., 1989). To delay the onset of immunodeficiency following HIV infection, the host must mount an HIV-specific immune response. One study showed that the presence of CTLs that recognized the HIV-1 envelope glycoprotein allowed the patient to control viral levels during primary infection since CTL recognition of infected cells can stimulate antiviral responses (Borrow et al., 1994). This recognition event requires the intracellular degradation of viral proteins and their assembly on an MHC-I for cell surface presentation.
Although CD4+ T-cells and monocytes are critical in HIV infection, antigen processing and presentation of HIV-1 epitopes have not been studied in these two cell types. Moreover, the type and efficacy of the immune response that those infected cell subsets can induce is unknown. Therefore, this study aimed to understand the antigen processing machinery in CD4+ T-cells and monocytes in the context of HIV-1 epitope production, degradation, and antigenicity. I focused on identifying the levels and kinetics of peptidases associated with the production and degradation of these peptide fragments and found that there were indeed differences in activity level and kinetics, which resulted in variations in the processing of HIV-1 epitopes. Observed heterogeneity in the identity and production time course of peptide products might have important consequences for CTL recognition of infected PBMC subsets and the ensuing immune response.
Using the intracellular fluorescence-based hydrolytic assay for peptidase activity, I showed how proteasome-caspase-like and trypsin-like, aminopeptidase, TOP, and TPPII activity levels and speed in CD4+ T-cells and monocytes differ for both healthy and HIV infected donors. We do not know whether these differences stem from variations in transcription or translation. Moreover, we do not understand what differences in intracellular peptidase quantity lead to observable differences in activity. Therefore, I cannot know if variations in activity come from increased expression of the proteins or from increased activity of each individual peptidase in monocytes. Another potential reason for the increase in peptidase activity in monocytes might be a contamination in the cytosol of cathepsins, endosomal peptidases that function in the exogenous pathway of antigen presentation. I corrected for this by stabilizing the pH of the whole cell extracts at 7.4, which considerably inhibits the function of cathepsins (Turk et al., 1999). Despite the uncertainty in the source of these functional differences between CD4+ T-cells and monocytes, the effects of these variations are of primary interest because we must fully understand the consequences for the immune response.
One limitation of this study was that it did not measure differences in non-cytosolic peptidases, such as ERAP-1 and ERAP-2, between the two PBMC subsets. Much of the literature about antigen processing of epitopes implicates ERAPs and not cytosolic peptidases (York et al., 2002). York and his group showed that purified ERAP-1 could trim all peptides that were longer than 10 residues and half of the 9-residue peptides. Though their experiment was not performed under the typical intracellular environment of the cell, it suggested that ERAP-1 was important in determining the loaded peptides for MHC-I presentation. My data on intracellular epitope processing does not conflict with the existing research, but it adds to the literature by showing that cytosolic peptidases can produce optimal HIV-1 epitopes from longer peptides. However, it is still important to study the potential differences in ERAP activity between the PBMC subsets as those peptidases can make the final peptide trimmings in the ER that may affect its loading on the MHC-I and CTL recognition.
Even with the exclusion of non-cytosolic peptidases, my study revealed differences among intracellular peptidases. For example, aminopeptidase showed the greatest fold differences in average activity values between CD4+ T-cells and monocytes. Compared to the proteasome, TOP, and TPPII, whose fold differences in the average activities between the subsets fell between 1.5 and 2.1, aminopeptidase activity in monocytes was about 3.5 times greater than that of CD4+ T-cells. This suggests that each peptidase can have unique differences in activity in different subsets. Also supporting this theory is the inconclusive result on the subset-specific differences in the proteasome-chymotrypsin active site. Although average activity levels of this active site were greater in monocytes, there was no statistically significant difference in values between the two cell types (data not shown). Other active sites within the proteasome showed significant subset-specific differences with 8 samples in the cohort, indicating non-uniform subset-specific variations in the proteasome active sites and perhaps, in most intracellular peptidases.
In addition to studying the differences in intracellular peptidase activity between the CD4+ T-cells and monocytes, it is important to study the entire antigen processing pathway because the presentation of the epitope is dependent on a multi-step process where each step can affect the identity of the final presented product. This process incorporates the kinetics of peptide production, degradation, transport, and MHC-I binding, which must be completed in a timely manner before the proliferating virions can escape. Here, I studied one of the earlier steps, intracellular epitope processing, which did not include epitope processing in the ER, peptide transport, or MHC-I binding kinetics. Products from intracellular processing need transport into the ER by TAP, which displays binding preferences for certain peptide sequences to transport into the ER (Procko and Gaudet, 2009). Thus, it is possible to infer that TAP might also influence the final product presented by MHC-I. Therefore, further experiments to discern the differences in the entire antigen processing pathway between CD4+ T-cells and monocytes must be completed.
Moreover, my research only focused on 2 subsets that HIV infects: CD4+ T-cells and monocytes, and did not include the entire repertoire of HIV-infectible PBMC subsets, such as macrophages, natural killer (NK) cells, and dendritic cells (DCs), which all contribute to infection (Weiss, 2008; Valentin et al., 2002). Macrophages are monocyte-derived cells that can store dormant virus in the gastrointestinal tract during chronic infection (Smith et al., 2003). Reduced levels of NK cells in infected patients are associated with worse prognoses (Ullum et al., 1995). DCs activate CTLs in cell-mediated immunity, making them especially important in HIV pathogenesis (Zarling et al., 1999). Although most of the infected cells in HIV-individuals are CD4+ T-cells, it is still important to understand the antigen processing capabilities of all PBMC subsets that contribute to this disease in order to understand the entire scope of presented HIV-1 peptides.
Apart from exposing peptidase activity differences between the subsets, my results introduce the concept of a balance between production and degradation of intracellular peptides within the infected cell. The observations that monocytes both produce and degrade optimal HIV-1 epitopes faster than CD4+ T-cells can be seemingly contradictory if one quickly attributes faster degradation of epitopes as a negative factor in antigen processing. However, one must remain aware of the balance between production, degradation, and transport of epitopes in the cytosol compartment. If monocytes have a more efficient transport system into the ER, then the increased degradation of optimal epitopes in monocytes might be negligible. Similarly, slower degradation of HIV-1 epitopes in CD4+ T-cells might allot more time for TAP binding and transport into the ER. With my results, I have reiterated the importance of kinetics in the entire antigen processing pathway and established the value of a balance between intracellular production and degradation of viral peptides destined to be presented to CTLs.
Additionally, I have shown that these observations of differential epitope processing might also be applicable to HIV-infected individuals. Though I did not perform the intracellular degradation of an HIV epitope on whole cell extracts from HIV-infected donors, the significant difference in peptidase activities between CD4+ T-cells and monocytes of HIV-infected samples suggests similarities in epitope production (Sup. Fig. C and D). However, the estimated frequency of infected CD4+ T-cells and monocytes waivers between 0.01% and 1% in an HIV-positive individual; therefore, the differences in peptidase activity between the subsets that I detected might stem from the uninfected cells (Poznansky et al., 1991; Smith et al., 2003). Although HIV epitope processing in infected cells remains unclear, a larger study that compares intracellular peptidase activity and epitope processing in HIV-infected PBMC will elucidate unknown patterns.
Furthermore, since HIV-1 has two main tropisms for PBMC cell subsets—T-cells and monocyte-derived cells—one can speculate that the untimely recognition of infected CD4+ T-cells may skew the viral population in an infected patient to the T-cell tropic strains. Recently, the prevalence of HIV-1 viral strains that preferentially infect CD4+ T-cells was found to be significantly greater in PBMC (Verhofstede et al., 2009). With increased viral levels of T-cell-tropic strains, greater numbers of CD4+ T-cells might be infected, thus contributing to the drastic reduction of CD4+ T-cells during HIV infection. Already, the period following infection before the CTL response has been identified as crucial for the virus to establish reservoirs in T-cells (Davenport et al., 2004). This study complements my findings to suggest that kinetics of antigen processing and presentation and CTL recognition are important for disease progression. Though it might be imprecise to generalize laboratory findings to the disease progression, my findings provide a different way of examining the dissimilar fates of CD4+ T-cells and monocytes during HIV infection.
This research is applicable to other viral infections, especially ones with tropism towards specific cell subsets. For the cell-mediated immune response to be effective, the infected cell must present a viral peptide that DC-primed CTLs can recognize. Since uninfected DCs can undergo cross-presentation, where extracellular peptides enter the endogenous pathway, it is important to ensure that the peptides presented by the infected cells are the same ones that the DCs can process for CTL priming. For example, one study on the DC-tropic human cytomegalovirus (HCMV) strain found that whole PBMC infected with HCMV could elicit more responses than infected DCs (Gerna et al., 2005). Here, differences in viral peptide presented might be the cause for differences in the elicited immune response. Further research on cell subsets infected by viruses with particular tropisms must be completed to understand the extent of cell-type-specific differences in antigen processing.
My results also pertain to the current literature on immunodominance of MHC-I restricted epitopes. For this purpose, I will define MHC-I immunodominance as the phenomenon where CTLs against a certain viral epitope are more prevalent in an infected individual. Mathematical models based on patient observations show that a dominant CTL response usually arises when a pathogen is homogeneous; however, this does not explain the molecular reasons for overrepresentation of that particular viral peptide (Nowak et al., 1995). A recent study on intracellular processing found that flanking sequences near the epitope contributed to the processing of those epitopes and strongly determined the intracellular peptide products (Le Gall et al., 2007). If cell subsets exhibit different kinetics in the production and identity of presented HIV-1 epitopes, then the immunodominant epitope might not be presented by one of the subsets. This would create dire consequences for the PBMC subset that does not present this dominant epitope, and clearance of infected cells in that subset might be compromised.
Besides understanding the immune response to infect CD4+ T-cells or monocytes, my findings also add to the existing literature on HIV vaccines. Unfortunately, the search for an HIV vaccine has not been very successful due to the complexity of HIV and its interactions with the immune system (Johnston and Fauci, 2007). Vaccines function by delivering antigen to a host; the antigen activates the immune system and induces the creation of antibody responses and antigen-specific memory T-cells, which persist in the host. Upon infection with that same pathogen, infected cells present foreign peptides that optimally trigger proliferation of those memory immune cells to perform their effector functions to curb the disease. An efficacious vaccine necessitates memory T-cells recognizing the viral peptides presented by infected cells. Since HIV infects various cell types that potentially have differences in their antigen processing machinery, a great candidate for a vaccine should contain a viral peptide that all cell types can process efficiently and present to CTLs. Thus, the consequences of subset-specific differences in antigen processing and presentation are broad reaching. The identity of the peptide presented might affect memory T-cell activation and CTL recognition of the infected cell.
Even without observations on other components of the antigen processing pathway or on other cells infected by HIV, my results demonstrate that differences in intracellular processing might affect the production, degradation, identity, and antigenicity of HIV-1 epitopes in two PBMC subsets. Monocytes showed increased activity in antigen processing peptidases, which might lead to a different repertoire of presented HIV-1 epitopes than CD4+ T-cells. The rapid production and larger amounts of more antigenic peptides in monocytes may increase the probability of transport into the ER for those peptides. If infected monocytes present more antigenic epitopes, then these cells might elicit a better immune response than infected CD4+ T-cells. Accordingly, it is important to further research on subset-specific differences in antigen processing not only to better understand the host’s immune response to infection but also to develop highly effective clinical interventions, such as vaccines.
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I would like to thank Sylvie Le Gall for giving me the opportunity to conduct research and for being a great mentor to me. Her dedication to the scientific process is a quality that I will always cherish. Furthermore, my experiments would have been impossible without the amazing supervision by Estibaliz (Esti) Lazaro. She helped me grow as both a researcher and as a young adult. Furthermore, I truly appreciate working in a world-class laboratory whose director, Bruce Walker, gave me encouragement and support during my time there. This project was supported in part by Howard Hughes Medical Institute (HHMI), the National Institutes of Health (NIH), and Bill and Melinda Gates Foundation.
I would also like to individually thank all members of the Le Gall lab, past and present: Sasha Blue Godfrey, Jeremy Ho, Christopher Kerrigan, Mei Zhang, Sergio Martinez, Paul Bourgine, Shao Chong Zhang, and Jonathan Chow for sharing their projects with me, assisting me with portions of my research, and staying positive in times of stress. Professor Losick’s HHMI program also supported me immensely in my research.
To end, my family and friends have encouraged me and kept me sane throughout this project. I cannot imagine completing this process without them. They always believed and still believe in my commitment to understanding and combating this destructive virus.