Rapamycin limits CD4+ T cell proliferation in simian immunodeficiency virus–infected rhesus macaques on antiretroviral therapy

Proliferation of latently infected CD4+ T cells with replication-competent proviruses is an important mechanism contributing to HIV persistence during antiretroviral therapy (ART). One approach to targeting this latent cell expansion is to inhibit mTOR, a regulatory kinase involved with cell growth, metabolism, and proliferation. Here, we determined the effects of chronic mTOR inhibition with rapamycin with or without T cell activation in SIV-infected rhesus macaques (RMs) on ART. Rapamycin perturbed the expression of multiple genes and signaling pathways important for cellular proliferation and substantially decreased the frequency of proliferating CD4+ memory T cells (TM cells) in blood and tissues. However, levels of cell-associated SIV DNA and SIV RNA were not markedly different between rapamycin-treated RMs and controls during ART. T cell activation with an anti-CD3LALA antibody induced increases in SIV RNA in plasma of RMs on rapamycin, consistent with SIV production. However, upon ART cessation, both rapamycin and CD3LALA–treated and control-treated RMs rebounded in less than 12 days, with no difference in the time to viral rebound or post-ART viral load set points. These results indicate that, while rapamycin can decrease the proliferation of CD4+ TM cells, chronic mTOR inhibition alone or in combination with T cell activation was not sufficient to disrupt the stability of the SIV reservoir.

Viruses. The SIVmac239M challenge stock used in this experiment was produced in transfected HEK-239T cells and the stock infectivity titer was determined using TZM-bl cells as previously described (2).
SIV viral detection assays. Plasma SIV RNA levels were determined using a gag-targeted quantitative real time/digital RT-PCR format assay, essentially as previously described, with 6 replicate reactions analyzed per extracted sample for assay threshold of 15 SIV RNA copies/ml (6). Ultrasensitive determinations of plasma SIV RNA were measured by concentrating virus from larger volumes of plasma by centrifugation. For ultrasensitive measurements, typically, 1.7 ml of plasma were centrifuged in a refrigerated microfuge (21,000 x g, 1 hr, 4° C) and nucleic acid was extracted from pellets as described (7) and quantitative RT PCR was performed with 12 reactions per extracted sample. Samples that did not yield any positive results across the replicate reactions were reported as a value of "less than" the value that would apply for one positive reaction out of 12 (6). As performed, the ultrasensitive assay provided a threshold sensitivity of 1 copy/ml plasma for a 1.7 ml sample. Quantitative assessment of SIV DNA and RNA in cells and tissues was performed using gag targeted nested quantitative hybrid real-time/digital RT-PCR and PCR assays, as previously described (6,8). SIV RNA or DNA copy numbers were normalized based on quantitation of a single copy rhesus genomic DNA sequence from the CCR5 locus from the same specimen to allow normalization of SIV RNA or DNA copy numbers per 10 6 diploid genome cell equivalents, as described (9). Ten replicate reactions were performed with aliquots of extracted DNA or RNA from each sample, with two additional spiked internal control reactions performed with each sample to assess potential reaction inhibition. Samples that did not yield any positive results across the replicate reactions were reported as a value of "less than" the value that would apply for one positive reaction out of 10. Threshold sensitivities for individual specimens varied as a function of the number of cells or amount of tissue available and analyzed.
LCV and RRV viral detection assays. Total DNA was purified from whole blood and resuspended in nuclease-free water. One hundred nanograms of total DNA were analyzed in duplicate by qPCR using primers and Taqman probe specific to the rLCV IR1 repeat region or to the RRV ORF3, as previously described (10).
The percentage of Glut1 + T cells was determined with the HighPlex FL v4.0.4 module using positive selection for CD3 and negative selection on green/red autofluorescence (to exclude RBCs, which strongly express Glut1). Each sample was manually double-checked to ensure accurate quantification.
Sequencing and qRT-PCR analysis of circulating miRNAs. Small RNAs were isolated from 200 ul archived plasma on days -14 and 42 post-rapamycin using the miRNeasy mini kit (Qiagen) and eluted into nuclease-free water. miRNA sequencing libraries were prepared using the NEBNext Small RNA Library Prep Set for Illumina (NEB) according to the manufacturer's protocol. Libraries were multiplexed and sequenced on the Illumina HiSeq2000 at the OHSU Massively Parallel Sequencing Shared Resource.
Mapped reads were subsequently analyzed by miRDeep2 (11) to obtain read counts per miRNA.
Determination of differentially expressed miRNAs was performed using the Bioconductor package, edgeR (12). Library sizes were normalized by using the edgeR default weighted trimmed mean of M values (TMM) method and pairwise comparisons were performed between two groups (i.e., rapamycin versus vehicle). miRNAs were considered to be significantly differentially expressed between groups when the false discovery rate (FDR) p-value was < 0.05. To analyze miRNA expression by qRT-PCR, six microliters of RNA were used in a 40 ul reverse transcriptase (RT) reaction with pooled stem-loop RT primers specific for miR-16, miR-18a, miR-21, miR-23a-3p, miR-26a, miR-28, miR-103-5p, and miR-155. qPCR reactions were set up in duplicate for each sample using 3 ul of the RT reaction and the corresponding miRNA Taqman probe. All miRNA qRT-PCR values were normalized to miR-16 and set relative to the control cohort at d-14. Raw sequence data files are accessible through NCBI SRA (short read archive), BioProject ID PRJNA772267.
RNA-Seq and bioinformatic analysis. RNA was purified from whole blood collected on days 84 and 181 post-rapamycin or vehicle control in Paxgene vacutainers using PreAnalytiX Paxgene Blood RNA kit (Qiagen), followed by RNASeq library generation using Illumina TruSeq Stranded Total RNA -Globin kit (Illumina). Paired-end sequencing reactions were run on an Illumina NextSeq 550 High-output platform (30M total reads per sample). Raw demultiplexed FASTQ paired end read files were trimmed of adapters and filtered using the program skewer (13) to discard those with an average phred quality score of less than 30 or a length of less than 36. Trimmed reads were then aligned using the HISAT2 (14) aligner to the Macaca mulatta NCBI reference genome assembly version Mmul_8 and sorted using SAMtools (15).
Aligned reads were counted and assigned to gene meta-features using the program featureCounts (16) as part of the Subread package. These count files were imported into the R programming language and were assessed for quality control, normalized and analyzed using an in-house pipeline utilizing the limma-trend method (17) for differential gene expression testing and the GSVA (18) library for gene set sample enrichment. Final differential gene expression lists were filtered to remove non-coding RNAs as well as LOC features. RNA-Seq data have been deposited in the NCBI's Gene Expression Omnibus database (GEO Accession # Pending).

Immunophenotyping.
To determine the phenotype of lymphocyte populations, whole blood or mononuclear cell preparations from LN and BM were stained for flow-cytometric analysis as previously described (19)(20)(21). Polychromatic (8-14 parameter) flow-cytometric analysis was performed on an LSR II   BD instrument using Pacific blue, BUV395, BUV495, BUV737, BUV805, BV421, BV510, BV570,   BV605, BV650, BV711, BV786, FITC, PE, PE-Texas red (PE-CF594), PE-Cy7, PerCP-Cy5.5, APC, APC-Cy7, and Alexa 700 as the available fluorescent parameters. Instrument setup and data acquisition procedures were performed as previously described (19)(20)(21). List mode multiparameter data files were analyzed using the FlowJo software program (Tree Star). Criteria for delineating TN and TM subsets and for setting positive (+) versus negative (−) markers for CCR5 and Ki-67 expression have been previously described in detail (19)(20)(21). In brief, TN constitute a uniform cluster of cells with a CD28 moderate , CCR7 + , CCR5 − , CD95 low phenotype, which is clearly distinguishable from the phenotypically diverse memory population that is CD95 high or displays one or more of the following non-naive phenotypic features: CD28 − ,  Figure 15); as one animal in the rapamycin group was lost from study prior to ART withdrawal due to non-study related health complications and therefore not included in the final analysis of these data.  . The WRS test was used to determine the significance of differences in AUC between the two treatment groups (p-values ≤ 0.05 are shown). Boxplots show jittered points and a box from 1st to 3rd quartiles (IQR) and a line at the median, with whiskers extending to the farthest data point within 1.5 × IQR above and below the box.  . The WRS test was used to determine the significance of differences in AUC between the two treatment groups.  Change in the proliferative fraction of (A) CD8 + TM, (B) CD20 + B cells and (C) NK cell subsets, including CD16 + CD56 -, CD16 -CD56 + and CD16 -CD56 -NK cells in blood of rapamycin treated RM (n = 6) vs. vehicle controls (n = 7) after ART withdrawal. Results are shown as mean (+ SEM) change from baseline of %Ki-67. The WRS test was used to determine the significance of differences in AUC between the two treatment groups (p-values ≤ 0.05 are shown). For these analyses n=13; one animal in the rapamycin group was lost from study just prior to ART withdrawal and was therefore not included in the final analysis.