Temporal response of the human virome to immunosuppression and antiviral therapy

Abstract

There are few substantive methods to measure the health of the immune system, and the connection between immune strength and the viral component of the microbiome is poorly understood. Organ transplant recipients are treated with posttransplant therapies that combine immunosuppressive and antiviral drugs, offering a window into the effects of immune modulation on the virome. We used sequencing of cell-free DNA in plasma to investigate drug-virome interactions in a cohort of organ transplant recipients (656 samples, 96 patients) and find that antivirals and immunosuppressants strongly affect the structure of the virome in plasma. We observe marked virome compositional dynamics at the onset of the therapy and find that the total viral load increases with immunosuppression, whereas the bacterial component of the microbiome remains largely unaffected. The data provide insight into the relationship between the human virome, the state of the immune system, and the effects of pharmacological treatment and offer a potential application of the virome state to predict immunocompetence.

Fig. 1. Study design, read statistics and phylogenetic distribution

A. Immunosuppression reduces the risk of rejection in transplantation but increases the risk of infection. B. Design of study. 656 plasma samples were collected, cell-free DNA was purified and sequenced to an average depth of 1.25 Gbp per sample. C. Number of samples collected as function of time for the different patient groups part of the study. D. Treatment protocol for patients in the study cohort, all patients are treated with maintenance immunosuppression (tacrolimus-based (TAC) for adult heart and lung transplant recipients and cyclosporine (CYC) for pediatric patients). CMV positive (donor or recipient, CMV+) transplant cases are treated with anti-CMV prophylaxis, valganciclovir (VAL). Mean level of tacrolimus measured in blood of transplant recipients treated with a TAC-based protocol (dashed line actual, solid line window average filter). E. Fraction of reads that remain after filtering of lower quality and duplicate reads (mean 86%, left) and after removal of human and low complexity reads (mean 2%, right). F. Relative genomic abundance at different levels of taxonomic classification after removal of human reads (average over all samples from all organ transplant recipients (n = 656)). The central piechart shows the composition at the superkingdom level of classification. Lower levels of classification are shown in donut charts with progressively larger radius. See also Figure S1.

Fig. 2. Relative viral genomic abundance as a function of drug dose and comparison to healthy reference

A. Mean virome composition for patients treated with the immunosuppressant tacrolimus (47 patients, 380 samples) as function of antiviral drug dose (valganciclovir) and concentration tacrolimus measured in blood. To account for the delayed effect of the virome composition on drug dose, the data on drug doses were window average filtered (window size 45 days, see fig. 1C). Herpesvirales and caudovirales dominate the virome when patients receive low doses of immunosuppressants and antiviral drugs. Conversely, anelloviridae dominate the virome when patients receive high doses of these drugs. B. Comparison of virome composition corresponding to healthy references (n = 9), post-transplant day one samples with low drug exposure, (n=13), and samples corresponding to high drug exposure (tacrolimus ≥ 9 ng/ml, valganciclovir ≥ 600 mg, n = 68). The virome structure for day one samples (1) and the virome structure measured for a set of healthy individuals (H) are distinct from the anellovirus-dominated distribution measured for samples corresponding to high drug doses (D). The piecharts show the mean fractions, p-values in boxplot based on the Mann-Whitney U test. C. Bray-Curtis beta diversity for all samples, among patients with the same transplant type (heart or lung), same age at time of transplant (10 year bins), within subjects, for patients treated with a similar drug dosage (tacrolimus level ± 0.5 ng/ml, valganciclovir ± 50 mg), and for samples collected from the same subjects within a one-month timespan. See also Figure S2.

Figure 3. Temporal dynamics of the microbiome composition post transplant

A. Relative abundance of dsDNA and ssDNA viruses for different time periods (average for all samples). The relative abundance of ssDNA viruses increases rapidly after the onset of the post-transplant drug therapy. After 6 months, the opposite trend is observed. B. Viral genome abundance at the family and order level of taxonomic classification for different time periods. Thefraction of anelloviridae expands rapidly in the first several months post-transplant. The fraction of herpesvirales,caudovirales and adenoviridae decreases in that same time period. After 6 months, the opposite trends are observed. C. Time-variation in the relative abundance of bacterial phyla. Compared to the viral abundance, the representationof different bacterial phyla remains relatively unchanged over the observed post transplant period. D. Shannonentropy as a measure of the within-sample alpha-diversity for bacterial and viral genera as function of time (datagrouped per one-month time periods). See also Figure S3.

Figure 4. Virome composition and total viral burden in the absence and presence of antiviral prophylaxis

A. Absolute viral load as a function of time, measured as viral genome copies per human genome copies detected by sequencing. Box plots are shown for different time periods with centers of the time periods marked on the x-axis. For all patient classes, the total viral load increases in the first weeks post transplant (black line is sigmoid fit, change in load 7.4 ± 3). B. Viral load and composition for CMV positive patients (prior CMV infection recipient and/or donor) that are treated with both immunosuppressants and antiviral drugs (78 patients, 543 samples). C. Viral load and composition for CMV negative patients, only treated with immunosuppressants (12 patients, 75 samples). See also Figure S4.

Figure 5. Lower anellovirus burden in patients that suffer from graft rejection

A. Time dependence of the anellovirus load in the subgroup of patients that suffer from a severe rejection episode (biopsy grade ≥ 2R/3A, red data, 20 patients, 177 time points) and in the subgroup of patients with a rejection-free post transplant course (biopsy grades < 2R/3A, blue data, 40 patients, 285 time points). Box plots are shown for different time periods with centers of the time periods marked on the x-axis. Solid lines are cubic splines (smoothing parameter 0.75). The inset shows a cartoon of the expected inverse relationship between the level of immunosuppression and the incidence of rejection and infection. B. Anellovirus load relative to the average load measured for all samples at the same time point. The time-normalized load for non-rejecting patients (N = 208) is compared to the load measured for patients suffering from a mild rejection event (biopsy grade 1R, N = 102) and patients suffering from a severe rejection episode (biopsy grade ≥ 2R/3A, N = 22). The p-values reflect the probability that the median viral load is higher for the subgroups at greater risk of rejection. The p-values are calculated by random sampling of the population with a greater amount of measurement points. N-fold random sampling, p = sum(median(A_rej) > median(A_non-rej))/N), where N=10⁴ and A_rej and A_non-rej are the relative viral loads for the populations at greater and lesser risk of rejection and non-rejecting respectively. C Test of the performance of the relative anellovirus load in classifying patients as non-rejecting vs. severely rejecting, receiver-operating characteristic curve, area under the curve = 0.72.