Estimating the contribution of CD4 T cell subset proliferation and differentiation to HIV persistence


Research cohort

The HOPE cohort consists of 37 PWH on suppressive ART (scientific and demographic info in Supplementary Desk 1), 24 of whom underwent a 45-day deuterium labeling research to measure CD4+ T cell turnover charges and had been reported beforehand17 in a cross-sectional research. Right here, we report a potential 3-year longitudinal evaluation of ranges of built-in HIV DNA in distinct maturational CD4 cell subsets from all 37 HOPE contributors and built-in these knowledge with measured CD4 cell subset turnover charges. Comply with up started 1–10 years after reaching viral suppression. Ranges of built-in HIV DNA per million CD4+ T cells tended to be secure over time inside people however differed between people by a number of orders of magnitude (Supplementary Fig. 1).

Quantifying HIV DNA in CD4+ T cell subsets

From these longitudinal samples, resting (HLA-DR-) CD4+ T cells had been remoted and sorted by movement cytometry into six CD4 cell subsets (kind schematic in Supplementary Fig. 2): naïve (TN), stem-cell reminiscence (TSCM), central reminiscence (TCM), transitional reminiscence (TTM), effector reminiscence (TEM) cells, and a putative terminally differentiated (TTD) inhabitants. As we noticed contamination with TN in TTD, the current evaluation was targeted on the primary 5 sorted populations, every of which was sorted with excessive purity17.

CD4+ T cell subset frequency was calculated because the ratio of subset cells per resting CD4 cells (Fig. 1A). TN and TCM had been most typical, every with a median throughout contributors and time of ~25% of all resting CD4 cells. The an infection frequency was then calculated because the variety of built-in HIV DNA copies per million resting cells inside every subset (Fig. 1B). Sometimes, ~1 in 1000 resting TTM and TEM harbored built-in HIV DNA, whereas the opposite subsets much less generally harbored HIV DNA16. Lastly, by multiplying the subset frequency by the an infection frequency, we derived the subset HIV DNA stage which displays the relative contribution of every subset to the measured HIV DNA, i.e., the variety of built-in HIV DNA copies in a given subset per million whole CD4 cells (Fig. 1C). Though not the very best in an infection frequency, given its excessive subset frequency, TCM contributed the very best median HIV DNA ranges, with ~100 contaminated TCM for each million CD4 cells. Median HIV DNA ranges had been usually decrease however not considerably completely different in different reminiscence phenotypes (TTM and TEM). Appreciable variability was famous inside every subset and for every knowledge kind.

Fig. 1: Definitions and illustration of research knowledge.
figure 1

From 37 PWH within the HOPE cohort, samples had been taken at 1–3 time factors over a 3-year interval. Resting CD4+ T cells had been sorted into 5 phenotypic subsets together with naïve (TN), stem-cell reminiscence (TSCM), central reminiscence (TCM), transitional reminiscence (TTM), and effector reminiscence cells (TEM). Three measurements had been noticed or calculated (panel headings): (A) subset frequency—the proportion of cells in every subset relative to whole resting CD4 cells (“different” represents resting cells not among the many 5 sorted subsets), (B) subset an infection frequency—built-in HIV DNA in every subset per million subset cells, and (C) subset HIV DNA—the variety of HIV DNA copies in a given subset per million CD4 cells. Coloured dots point out values from all participant  time factors and black diamonds symbolize means throughout all dots.

HIV contaminated cells decay quicker than non-infected cells in TTM and TEM (however not different) subsets

To find out if HIV DNA cleared in another way in every subset, we used a statistical framework (log-linear blended results mannequin) to evaluate adjustments in subset an infection frequency over the 3-year research interval (Fig. 2A). Though the decay charges had been heterogeneous (and even constructive, i.e., rising, in sure people), the common built-in DNA ranges inside TN, TSCM, and TCM didn’t considerably change over time (t take a look at p > 0.05 in opposition to null speculation of no change), whereas these inside TTM and TEM decayed slowly however considerably over time (t take a look at p < 1e-8) (Fig. 2B). Accordingly, TN and TCM charges had been considerably completely different from TTM and TEM charges (pairwise t checks p < 0.005 in accordance with Bonferroni correction for a number of comparisons). Estimated median half-lives had been 81 and 59 months for TTM and TEM, respectively. A declining subset an infection frequency implies that HIV-infected cells decay quicker than non-HIV-infected cells in that subset, suggesting an lively course of whereby HIV-infected cells are selectively eliminated.

Fig. 2: The kinetics of subset HIV frequency differ by subset and are usually slower than mobile turnover.
figure 2

A Longitudinal kinetics of HIV subset an infection frequency in every cell subset: skinny strains and dots are particular person trajectories and thick stable strains symbolize the estimated common slopes from a log-linear blended results mannequin. B Field plots of contributors’ decay charges—observe that some are constructive, which means that HIV frequency elevated. P-values point out one-sided t-test in opposition to null speculation of no clearance. For scale, the decay charge equal to the QVOA reservoir benchmark 44 month half-life30 is denoted with the dashed grey line. C Mobile turnover charges derived from deuterated water labeling in 24 of those 37 people. P values indicated paired two-sided t-tests with non-equal variance. Magnitudes of mobile turnover charges (in non-TN subsets) are a lot greater than HIV decay charges—observe distinction in y-axis scales in (C) versus (B). D The % of mobile turnover that’s accompanied by HIV turnover (Strategies). Values near 100% point out that HIV is often repopulated when cells flip over. In (BD) field plots point out median (middle line), interquartile vary (field), 1.5x interquartile vary (whiskers), and outliers (grey diamonds). Every dot (N = 24) represents a person. E Cartoon instance for TEM: in a 12 months, there may be frequent mobile turnover, which is occasionally (~5% of occasions) accompanied by elimination of HIV-infected cells, ensuing within the noticed slight decay of HIV DNA.

Measuring mobile turnover by way of deuterium labeling

We used deuterated water labeling37 carried out on 24 of the 37 HOPE contributors to estimate mobile turnover charges in every subset17. In these experiments, the mobile turnover charge is derived from modeling the proportion of cells that take up a deuterium label throughout a 45-day labeling interval (mannequin schematic in Supplementary Fig. 3). Extra particularly, the fraction of cells that divided throughout publicity to deuterated water is calculated37,38,39. Though what’s initially measured from deuterium incorporation into genomic DNA is S-phase cell division, or proliferation40, we as an alternative use the time period turnover charge right here as a result of this charge represents the mixture of all mechanisms that impression ranges of deuterium in a subset, together with migration/trafficking and/or differentiation. For example, labels in a given subset can rise attributable to maturation of a labeled progenitor cell or fall attributable to additional maturation41. Mobile turnover charges ranged throughout subsets from slowest (TN median 0.2/12 months) to most speedy (TEM median 2.6/years) (Fig. 2C). Turnover charges had been usually extra speedy in additional differentiated subsets, with the best variations between TN to TSCM and TCM to TTM (pairwise t-test p-values in Fig. 2C). A turnover charge of 1 per 12 months corresponds to a half-life of 8.3 months, so these CD4 subsets have median half-lives of 35, 5.3, 4.3, 3.4, and three.1 months, respectively. Appreciable variability was famous inside every subset.

CD4+ T cell turnover is usually however not at all times accompanied by HIV DNA turnover in sure subsets

In all subsets besides TN, the mobile turnover charge was roughly an order of magnitude quicker than the speed of decay of HIV-infected cells (evaluate Fig. 2B, C). This implies that mobile turnover of HIV-infected cells doesn’t normally end in removing of HIV DNA. We due to this fact estimated the proportion of mobile turnover occasions that may even be accompanied by HIV turnover slightly than HIV clearance (Strategies). For the 5 subsets respectively, we calculated medians of 112, 94, 99, 96, and 94% (Fig. 2D). In TN, this quantity is bigger than 100% suggesting some will increase in HIV DNA on this subset; nevertheless, there was very excessive variability throughout contributors making the median much less dependable. Moreover, the a lot decrease mobile turnover charges invoke decrease sign in comparison with noise within the deuterium labeling measurements, probably lowering precision. Within the TCM subset, we estimate that mobile turnover virtually at all times ends in HIV turnover, so HIV DNA doesn’t essentially decline. Lastly, in TSCM, TTM, and TEM, 94–96% of mobile turnover might be related to HIV turnover. That’s, roughly 5% of mobile turnover occasions are accompanied by clearance of HIV DNA in these subsets (see instance for TEM in Fig. 2E). Collectively, these outcomes point out that almost all, however not all, occasions that improve cell numbers—mobile proliferation and different mechanisms contributing to turnover—are accompanied by concomitant will increase in HIV DNA. Any slight imbalance in the direction of cell quantity will increase with out HIV will increase may drive decay of HIV DNA in sure CD4 subsets.

Mechanistic modeling of subset HIV DNA suggests differentiation quickly passages HIV by CD4+ T cell subset maturation pathways

CD4+ T cell subsets are linked to at least one one other by recognized steps of lineage maturation14. Beforehand, on this cohort, we discovered HIV DNA built-in into similar human chromosomal websites amongst TCM and TTM and TTM and TEM subsets, a robust signal that differentiation of HIV-infected cells can happen17. Furthermore, HIV DNA frequencies and ranges had been discovered to correlate between sure subsets (Supplementary Fig. 4). But, the relative diploma to which differentiation right into a given CD4 cell subset versus proliferation inside that subset contributes to HIV DNA persistence stays unclear. Due to this fact, we subsequent sought to mannequin HIV DNA ranges with a mechanistic mannequin that included particular guidelines of mobile proliferation, demise, and differentiation.

We developed a wide range of fashions inclusive of various mechanistic processes and levels of complexity (Desk 1, see Strategies for equations and textual content describing assumptions). The record of fashions encodes situations by which HIV DNA ranges are ruled by a number of mechanisms together with gradual decay, proliferation, and cell differentiation between subsets. A schematic and desk of definitions illustrates the charges we think about (Fig. 3A, B). We then examined these fashions for match in opposition to ranges of subset HIV DNA (e.g., Fig. 1C). Importantly, this can be a completely different knowledge kind than in Fig. 2 and offers a typical denominator of million CD4+ T cells for every subset. In our mannequin, the degrees of HIV DNA are linked throughout subsets, permitting proliferation and differentiation charges to be instantly in contrast.

Desk 1 Outcomes of knowledge theoretic mathematical mannequin choice on built-in HIV DNA per million CD4+ T cells
Fig. 3: Modeling subset HIV DNA dynamics by way of physiological mechanisms of T cells together with proliferation, differentiation, and demise.
figure 3

Mannequin schematic (A) and definitions (B) of mannequin charges for a single subset. Web impact charges ({{{{Theta }}}}) describes the entire kinetic charge summing all modeled mechanisms governing HIV DNA so might be constructive or detrimental for every subset. The turnover charge represents the constructive contribution to mobile turnover, estimated by way of the labeling research. Our mathematical mannequin estimates the repopulation (θ) and differentiation (φ) charges out and in of every subset. Due to this fact, we will calculate the proliferation (α) and demise (δ) charges for every subset from turnover and differentiation. C Essentially the most parsimonious mannequin of all mixed subset HIV DNA ranges included contaminated cell proliferation (dots flashing), demise (dots falling and fading), and differentiation between sure subsets (dots shifting). This picture is a screenshot of the Supplementary Film 1 which visualizes the system over time. The differentiation sample that was most parsimonious included a basic movement from least to most mature subsets, but in addition some “skip” patterns, i.e., TN-to-TCM and TCM-to-TEM. With no additional measured subset previous TEM, demise and differentiation out couldn’t be distinguished for TEM so we mixed the 2 phenomena (see *).

Fashions had been ranked by their accuracy (match to knowledge) but in addition penalized for complexity utilizing info criterion. The chosen mannequin (Fig. 3C, Supplementary Film 1) ranked greatest by each Akaike and Bayesian info standards42 (AIC and BIC, Desk 1). On this greatest mannequin, every subset stage of HIV DNA ({H}_{s}) has a repopulation charge ({theta }_{s}) that encapsulates the stability of cell proliferation and demise. Mobile differentiation passages HIV DNA between subsets (i) to (j) with charge ({phi }_{i:j}). As a result of we didn’t embrace the terminally differentiated subset (TTD) attributable to TN experimental contamination, we couldn’t estimate TEM clearance and differentiation charges concurrently. Due to this fact, we explicitly observe a mixture of the 2 phenomena (see * in Fig. 3C). We additionally constrained parameter estimation to make sure charges for every subset had been no bigger than noticed mobile turnover charges for that subset (Supplementary Fig. 5A, B). When this constraint on parameter area was relaxed, some fashions carried out barely higher, however our preliminary greatest mannequin remained second solely to a mannequin with the identical construction however together with biologically unrealistic charges (Desk 1). Due to this fact, for the rest of the evaluation, we proceeded with this extra conservative mannequin.

Qualitative options of mannequin choice present a number of mechanistic outcomes. First, all fashions missing differentiation had considerably poorer match in comparison with the optimum mannequin (ΔAIC > 2, Desk 1). A mannequin that tried to elucidate HIV ranges by differentiation with out cell proliferation was considerably worse than the optimum mannequin (ΔAIC = 85). The chosen mannequin contains passaging of HIV DNA alongside CD4 maturation pathways (i.e., linearly from least to most differentiated subsets) however moreover was improved by the addition of “skip” differentiation from TN to TCM, and from TCM to TEM. An easier mannequin with purely linear differentiation TN > TSCM > TCM > TTM > TEM was ranked third however didn’t present as robust a match to knowledge (Desk 1). Collectively, these findings counsel differentiation is important however not ample to exactly describe HIV DNA dynamics in CD4 cell subsets over time.

To probably broaden the applicability of this mannequin, we offer a desk of preliminary circumstances, imply and normal deviation of inhabitants charges, and estimated variability of HIV DNA knowledge (Supplementary Desk 2).

Sensitivity evaluation on mannequin choice

To evaluate whether or not the sparse 3-year sampling may have resulted in observations favoring a mannequin with skip differentiation, we simulated the best-fit model of the mannequin with linear differentiation, added applicable noise, and sampled time factors per the 3-year research scheme (Supplementary Fig. 6). We then refit this mannequin to the linear- and skip-differentiation fashions. As anticipated, the linear differentiation mannequin match these knowledge higher than the skip-differentiation mannequin (ΔLL = 1.5, ΔAIC = 10 in comparison with skip-differentiation mannequin). This sensitivity evaluation illustrates how mannequin choice might be self-consistent, such that knowledge generated with a given mannequin incorporates sufficient info to recuperate the identical mannequin by way of mannequin choice. As well as, it supported that the skip differentiation mannequin was not innately favored based mostly on noise or the sampling scheme.

Estimating HIV DNA decay half-lives within the mannequin inclusive of mobile differentiation

With some exceptions, mannequin matches had been glorious throughout extremely variable subset trajectories (see 18 of 37 matches for contributors with three time factors, Fig. 4A). The general inhabitants tendencies for every subset present that, however some extent of heterogeneity, the common built-in HIV DNA stage decays per million CD4+ T cells in 4/5 subsets with a half-life of: 4.3 years in TN, 2.6 years in TSCM, 3.2 years in TCM and three.7 years in TEM (Fig. 4B). On the similar time, HIV DNA ranges in TTM appeared to extend (which means no half-life). When HIV DNA ranges in all subsets had been summed, the online half-life throughout all subsets was calculated to be 5.4 years. Though these knowledge aren’t inclusive of all CD4 cell subsets able to harboring HIV genomes, and people have completely different timeframes of ART (i.e., see trajectories in Supplementary Fig. 1), these half-life estimates are inside ranges of previously-estimated HIV DNA decay22,32,43.

Fig. 4: Modeling together with proliferation and differentiation recapitulates particular person subset HIV DNA kinetics.
figure 4

A Mannequin matches (stable strains) of subset HIV DNA ranges (dots/dashed strains) for all contributors having 3 longitudinal measurements (N = 18). B Inhabitants mannequin (stable strains) estimates of subset HIV DNA (copies per million CD4 T cells) to all longitudinal participant knowledge (dots with skinny strains).

Quantifying the contribution of cell proliferation, demise, and differentiation to built-in HIV DNA persistence

To match and distinction the mechanisms underlying HIV persistence in the very best mannequin, we subsequent instantly utilized the mobile turnover knowledge to estimate absolutely the variety of built-in HIV DNA copies (per million CD4+ T cells) that enter and go away every subset pool throughout a typical 12 months attributable to proliferation, differentiation out and in, and demise (Strategies, Fig. 3A, B).

In a typical 12 months within the common particular person, we calculated (Strategies) that 1–10 HIV DNA copies per million CD4 T cells are generated by proliferation of TN and TSCM whereas 100–1000 copies are generated by proliferation in TCM, TTM, and TEM (Fig. 5B). In the meantime, related numbers of HIV DNA copies are eliminated by demise (Fig. 5D). These numbers suggest that HIV DNA persists in a speedy and dynamic near-equilibrium state (Supplementary Film 1). On the similar time, few HIV DNA copies per million CD4+ T cells enter TN and TSCM (Fig. 5A), and 1–10 copies exit these subsets (Fig. 5C) attributable to differentiation. On common, ten copies enter, and 100 copies go away TCM attributable to differentiation (Fig. 5C). The unequal differentiation out and in then requires a slight imbalance favoring proliferation over demise (Fig. 5B vs. Fig. 5D) to take care of TCM close to equilibrium. TTM differentiation was virtually balanced (imply ~100 copies in, ~70 copies out in Fig. 5A vs. Fig. 5C). We couldn’t distinguish TEM outward differentiation from demise utilizing these knowledge since terminally differentiated cells weren’t studied on this evaluation. Appreciable variability was famous throughout contributors inside every subset.

Fig. 5: Absolute and relative contribution to HIV reservoirs by cell proliferation, demise, and differentiation.
figure 5

AD Absolute contributions to HIV subset DNA by differentiation in, proliferation, differentiation out, and demise of every subset. E Relative contribution of every mechanism to every subset. Optimistic (persistence) and detrimental (clearance) contributions are handled individually for % calculations. Differentiation out and demise of TEM are grouped collectively as a result of the dearth of terminally differentiated cells on this evaluation precluded identification of each charges. In AE, estimate for every particular person (N = 24) are proven as coloured dots and black diamonds point out means throughout people. F Absolutely the contribution of every mechanism averaged throughout all people.

Subsequent, we in contrast mechanisms relative to at least one one other by calculating the proportion of creation (differentiation in and proliferation) and removing (differentiation out and demise) occasions from every mechanism and for every cell subset (Fig. 5E). Proliferation was the dominant mechanism contributing to the persistence of built-in HIV DNA in TN, TCM, and TTM. Nonetheless, differentiation inward could play an vital function in sustaining HIV genomes in TSCM and TEM. Differentiation outward was an vital mechanism significantly for TN and TCM, by which removing was projected to happen extra by differentiation than demise. TEM are recognized to proliferate often and had the very best mobile turnover charges. Nonetheless, absolutely the contribution of proliferation estimated right here was decrease than differentiation in. If HIV DNA dynamics mirror mobile dynamics measured with deuterated water experiments, this means that mobile turnover of HIV-infected TEM could significantly be influenced by differentiation.

In abstract the mannequin portrays typical HIV DNA ranges as a quickly proliferating, dying, and differentiating inhabitants that, in mixture, maintains a virtually equilibrated system such that built-in HIV DNA solely decays slowly and solely in additional mature CD4 subsets (Supplementary Film 1). Importantly, proliferation stays the predominant mechanism within the technology of built-in HIV DNA. TN and TSCM comprise much less HIV DNA; due to this fact, absolutely the HIV DNA creation and removing in these subsets is orders of magnitude smaller than that present in reminiscence subsets. Proliferation is of specific impression within the context of TCM and TTM: when coupled with differentiation outward (to a number of subsets), these subsets contribute meaningfully to HIV DNA persistence within the quickly dying/differentiating TEM pool (Fig. 5F).

Modeling cell-associated HIV RNA

We additionally match fashions with no differentiation, linear differentiation, and our favored mannequin with skip differentiation to cell-associated HIV RNA (caRNA) ranges measured in the identical contributors (Supplementary Fig. 7). For these knowledge, the mannequin with out differentiation was optimum by way of AIC. Consistent with observations for HIV DNA, caRNA ranges per million CD4 T cells appeared to extend barely in TCM and reduce in TEM in these contributors. However not like for DNA, RNA elevated in TTM. Collectively, these knowledge counsel that RNA ranges are much less tightly linked throughout subsets, probably as a result of RNA is generated by DNA and extra variability on this course of reduces correlations.

In silico knockout demonstrates the theoretical capability of reservoir discount by diminished cell proliferation and/or enhanced cell differentiation

Mechanistic modeling offers the precious capability to challenge the dynamics of HIV DNA persistence within the context of perturbed CD4+ T cell subset proliferation and/or differentiation. Thus, we used the mannequin to simulate three therapeutic situations over a interval of three years: ART alone (Fig. 6A), ART with anti-proliferative remedy that reduces mobile proliferation for all subsets by an element of two (Fig. 6B), and ART with enhanced differentation remedy that will increase differentiation for all subsets by an element of two (Fig. 6C). We calculated adjustments in HIV DNA per million CD4+ T cells over time. For ART alone, (as noticed within the uncooked experimental knowledge) we projected a comparatively minimal median change and extensive variability inclusive of will increase and reduces in all subsets. For ART and anti-proliferative remedy, median HIV DNA throughout subsets dropped by 300 copies (or ~90%) with most simulations leading to general lower. For ART and enhanced differentiation remedy, median HIV DNA throughout subsets dropped by 200-300 copies (or ~80–90%) with barely extra simulations inclusive of no change or improve versus anti-proliferative remedy.

Fig. 6: Simulations of modulated HIV persistence mechanisms.
figure 6

Projections of subset HIV DNA ranges in all resting CD4+ T cell subsets throughout three theoretical therapeutic interventions: A ART alone, (B) ART and anti-proliferative remedy: 2-fold discount in cell proliferation in all subsets, and (C) ART and enhanced differentiation remedy: 2-fold improve in cell differentiation out and in of all subsets. Field plots point out median (middle line), interquartile vary (field), 1.5x interquartile vary (whiskers), and outliers (open circles). Every line (N = 24) represents a simulation utilizing parameters from every particular person.


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