Abstract Mycobacterium tuberculosis (Mtb) readily aggregates in culture and Mtb aggregates in the lung were observed in experimental Mtb infection. However, the physiological consequences of Mtb aggregation are incompletely understood. Here we examined the human macrophage transcriptional response to aggregated Mtb relative to infection with non-aggregated single or multiple bacilli per host cell. Infection with aggregated Mtb led to an early upregulation of pro-inflammatory associated genes and enhanced TNF α signaling via the NF κ B pathway. Both these pathways were significantly upregulated relative to infection with single bacilli, and TNF α signaling was also significantly elevated relative to infection with multiple non-aggregated Mtb. Secretion of TNF α and downstream cytokines were also enhanced. On a longer timescale, aggregate infection led to overall increased acidification per macrophage and a high proportion of death in these cells after aggregate phagocytosis. Host cell death did not occur when Mtb aggregates were heat killed despite such clumps being readily picked up. To validate that Mtb aggregates do occur in the human lung, we document Mtb aggregates surrounding a cavity in a human TB lesion. Aggregates may therefore be present in some lesions and elicit a stronger inflammatory response resulting in recruitment of additional phagocytes and their subsequent death, potentially leading to necrosis and transmission.
Abstract The SARS-CoV-2 Omicron BA.1 variant emerged in late 2021 and is characterised by multiple spike mutations across all spike domains. Here we show that Omicron BA.1 has higher affinity for ACE2 compared to Delta, and confers very significant evasion of therapeutic monoclonal and vaccine-elicited polyclonal neutralising antibodies after two doses. mRNA vaccination as a third vaccine dose rescues and broadens neutralisation. Importantly, antiviral drugs remdesevir and molnupiravir retain efficacy against Omicron BA.1. We found that in human nasal epithelial 3D cultures replication was similar for both Omicron and Delta. However, in lower airway organoids, Calu-3 lung cells and gut adenocarcinoma cell lines live Omicron virus demonstrated significantly lower replication in comparison to Delta. We noted that despite presence of mutations predicted to favour spike S1/S2 cleavage, the spike protein is less efficiently cleaved in live Omicron virions compared to Delta virions. We mapped the replication differences between the variants to entry efficiency using spike pseudotyped virus (PV) entry assays. The defect for Omicron PV in specific cell types correlated with higher cellular RNA expression of TMPRSS2, and accordingly knock down of TMPRSS2 impacted Delta entry to a greater extent as compared to Omicron. Furthermore, drug inhibitors targeting specific entry pathways demonstrated that the Omicron spike inefficiently utilises the cellular protease TMPRSS2 that mediates cell entry via plasma membrane fusion. Instead, we demonstrate that Omicron spike has greater dependency on cell entry via the endocytic pathway requiring the activity of endosomal cathepsins to cleave spike. Consistent with suboptimal S1/S2 cleavage and inability to utilise TMPRSS2, syncytium formation by the Omicron spike was dramatically impaired compared to the Delta spike. Overall, Omicron appears to have gained significant evasion from neutralising antibodies whilst maintaining sensitivity to antiviral drugs targeting the polymerase. Omicron has shifted cellular tropism away from TMPRSS2 expressing cells that are enriched in cells found in the lower respiratory and GI tracts, with implications for altered pathogenesis.
HIV-1 anti-retroviral therapy is highly effective but fails to eliminate a reservoir of latent proviruses leading to a requirement for life-long treatment. How the site of integration of authentic intact latent proviruses might impact their own or neighboring gene expression or reservoir dynamics is poorly understood. Here we report on proviral and neighboring gene transcription at sites of intact latent HIV-1 integration in cultured T cells obtained directly from people living with HIV, as well as engineered primary T cells and cell lines. Proviral gene expression was correlated to the level of endogenous gene expression under resting but not activated conditions. Notably, latent proviral promoters were 10010,000X less active than in productively infected cells and had little or no measurable impact on neighboring gene expression under resting or activated conditions. Thus, the site of integration has a dominant effect on the transcriptional activity of intact HIV-1 proviruses in the latent reservoir thereby influencing cytopathic effects and proviral immune evasion.
Abstract The SARS-CoV-2 B.1.617.2 (Delta) variant was first identified in the state of Maharashtra in late 2020 and has spread throughout India, displacing the B.1.1.7 (Alpha) variant and other pre-existing lineages. Mathematical modelling indicates that the growth advantage is most likely explained by a combination of increased transmissibility and immune evasion. Indeed in vitro, the delta variant is less sensitive to neutralising antibodies in sera from recovered individuals, with higher replication efficiency as compared to the Alpha variant. In an analysis of vaccine breakthrough in over 100 healthcare workers across three centres in India, the Delta variant not only dominates vaccine-breakthrough infections with higher respiratory viral loads compared to non-delta infections (Ct value of 16.5 versus 19), but also generates greater transmission between HCW as compared to B.1.1.7 or B.1.617.1 (p=0.02). In vitro, the Delta variant shows 8 fold approximately reduced sensitivity to vaccine-elicited antibodies compared to wild type Wuhan-1 bearing D614G. Serum neutralising titres against the SARS-CoV-2 Delta variant were significantly lower in participants vaccinated with ChadOx-1 as compared to BNT162b2 (GMT 3372 versus 654, p<0001). These combined epidemiological and in vitro data indicate that the dominance of the Delta variant in India has been most likely driven by a combination of evasion of neutralising antibodies in previously infected individuals and increased virus infectivity. Whilst severe disease in fully vaccinated HCW was rare, breakthrough transmission clusters in hospitals associated with the Delta variant are concerning and indicate that infection control measures need continue in the post-vaccination era.
Mycobacterium tuberculosis (Mtb) bacilli readily aggregate. We previously reported that Mtb aggregates lead to phagocyte death and subsequent efficient replication in the dead infected cells. Here, we examined the transcriptional response of human monocyte derived macrophages to phagocytosis of aggregated Mtb relative to phagocytosis of non-aggregated single or multiple bacilli. Infection with aggregated Mtb led to an early upregulation of pro-inflammatory associated genes and enhanced TNFα signaling via the NFκB pathway. These pathways were significantly more upregulated relative to infection with single or multiple non-aggregated bacilli per cell. Phagocytosis of aggregates led to a decreased phagosome acidification on a per bacillus basis and increased phagocyte cell death, which was not observed when Mtb aggregates were heat killed prior to phagocytosis. Mtb aggregates, observed in a granuloma from a patient, were found surrounding a lesion cavity. These observations suggest that TB aggregation may be a mechanism for pathogenesis. They raise the possibility that aggregated Mtb, if spread from individual to individual, could facilitate increased inflammation, Mtb growth, and macrophage cell death, potentially leading to active disease, cell necrosis, and additional cycles of transmission.
Abstract The SARS-CoV-2 B.1.617.2 (Delta) variant was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha). In vitro , B.1.617.2 is 6-fold less sensitive to serum neutralising antibodies from recovered individuals, and 8-fold less sensitive to vaccine-elicited antibodies as compared to wild type Wuhan-1 bearing D614G. Serum neutralising titres against B.1.617.2 were lower in ChAdOx-1 versus BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies against the receptor binding domain (RBD) and N-terminal domain (NTD), in particular to the clinically approved bamlavinimab and imdevimab monoclonal antibodies. B.1.617.2 demonstrated higher replication efficiency in both airway organoid and human airway epithelial systems as compared to B.1.1.7, associated with B.1.617.2 spike being in a predominantly cleaved state compared to B.1.1.7. Additionally we observed that B.1.617.2 had higher replication and spike mediated entry as compared to B.1.617.1, potentially explaining B.1.617.2 dominance. In an analysis of over 130 SARS-CoV-2 infected healthcare workers across three centres in India during a period of mixed lineage circulation, we observed substantially reduced ChAdOx-1 vaccine efficacy against B.1.617.2 relative to non-B.1.617.2. Compromised vaccine efficacy against the highly fit and immune evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era.
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Appendix 1 References Decision letter Author response Article and author information Metrics Abstract HIV has been reported to be cytotoxic in vitro and in lymph node infection models. Using a computational approach, we found that partial inhibition of transmissions of multiple virions per cell could lead to increased numbers of live infected cells. If the number of viral DNA copies remains above one after inhibition, then eliminating the surplus viral copies reduces cell death. Using a cell line, we observed increased numbers of live infected cells when infection was partially inhibited with the antiretroviral efavirenz or neutralizing antibody. We then used efavirenz at concentrations reported in lymph nodes to inhibit lymph node infection by partially resistant HIV mutants. We observed more live infected lymph node cells, but with fewer HIV DNA copies per cell, relative to no drug. Hence, counterintuitively, limited attenuation of HIV transmission per cell may increase live infected cell numbers in environments where the force of infection is high. https://doi.org/10.7554/eLife.30134.001 eLife digest The HIVvirus infects cells of the immune system. Once inside, it hijacks the cellular molecular machineries to make more copies of itself, which are then transmitted to new host cells. HIV eventually kills most cells it infects, either in the steps leading to the infection of the cell, or after the cell is already producing virus. HIV can spread between cells in two ways, known as cell-free or cell-to-cell. In the first, individual viruses are released from infected cells and move randomly through the body in the hope of finding new cells to infect. In the second, infected cells interact directly with uninfected cells. The second method is often much more successful at infecting new cells since they are exposed to multiple virus particles. HIV infections can be controlled by using combinations of antiretroviral drugs, such as efavirenz, to prevent the virus from making more of itself. With a high enough dose, the drugs can in theory completely stop HIV infections, unless the virus becomes resistant to treatment. However, some patients continue to use these drugs even after the virus they are infected with develops resistance. It is not clear what effect taking ineffective, or partially effective, drugs has on how HIV progresses. Using efavirenz, Jackson, Hunter et al. partially limited the spread of HIV between human cells grown in the laboratory. The experiments mirrored the situation where a partially resistant HIV strain spreads through the body. The results show that the success of cell-free infection is reduced as drug dose increases. Yet paradoxically, in cell-to-cell infection, the presence of drug caused more cells to become infected. This can be explained by the fact that, in cell-to-cell spread, each cell is exposed to multiple copies of the virus. The drug dose reduced the number of viral copies per cell without stopping the virus from infecting completely. The reduced number of viral copies per cell made it more likely that infected cells would survive the infection long enough to produce virus particles themselves. Viruses that can kill cells, such as HIV, must balance the need to make more of themselves against the speed that they kill their host cell to maximize the number of infected cells. If transmission between cells is too effective and too many virus particles are delivered to the new cell, the virus may not manage to infect new hosts before killing the old ones. These findings highlight this delicate balance. They also indicate a potential issue in using drugs to treat partially resistant virus strains. Without care, these treatments could increase the number of infected cells in the body, potentially worsening the effects of living with HIV. https://doi.org/10.7554/eLife.30134.002 Introduction HIV infection is known to result in extensive T cell depletion in lymph node environments (Sanchez et al., 2015), where infection is most robust (Brenchley et al., 2004; Doitsh et al., 2010; Doitsh et al., 2014; Finkel et al., 1995; Galloway et al., 2015; Mattapallil et al., 2005). Depletion of HIV infectable target cells, in addition to onset of immune control, is thought to account for the decreased replication ratio of HIV from an initial peak in early infection (Bonhoeffer et al., 1997; Nowak and May, 2000; Perelson, 2002; Phillips, 1996; Quiñones-Mateu and Arts, 2006; Ribeiro et al., 2010; Wodarz and Levy, 2007). This is consistent with observations that individuals are most infectious in the initial, acute stage of infection, where the target cell population is relatively intact and produces high viral loads (Hollingsworth et al., 2008; Wawer et al., 2005). T-cell death occurs by several mechanisms, which are either directly or indirectly mediated by HIV infection. Accumulation of incompletely reverse transcribed HIV transcripts is sensed by interferon-γ–inducible protein 16 (Monroe et al., 2014) and leads to pyroptotic death of incompletely infected cells by initiating a cellular defence program involving the activation of caspase 1 (Doitsh et al., 2010; Doitsh et al., 2014; Galloway et al., 2015). HIV proteins Tat and Env have also been shown to lead to cell death of infected cells through CD95-mediated apoptosis following T-cell activation (Banda et al., 1992; Westendorp et al., 1995a; Westendorp et al., 1995b1995). Using SIV infection, it has been shown that damage to lymph nodes due to chronic immune activation leads to an environment less conducive to T-cell survival (Zeng et al., 2012). Finally, double strand breaks in the host DNA caused by integration of the reverse transcribed virus results in cell death by the DNA-PK-mediated activation of the p53 response (Cooper et al., 2013). The lymph node environment is conducive to HIV infection due to: (1) presence of infectable cells (Deleage et al., 2016; Embretson et al., 1993; Tenner-Racz et al., 1998); (2) proximity of cells to each other and lack of flow which should enable cell-to-cell HIV spread (Baxter et al., 2014; Dale et al., 2011; Groot et al., 2008; Groppelli et al., 2015; Gummuluru et al., 2002; Hübner et al., 2009; Jolly et al., 2004; Jolly et al., 2011; Münch et al., 2007; Sherer et al., 2007; Sourisseau et al., 2007; Sowinski et al., 2008); (3) decreased penetration of antiretroviral therapy (ART) (Fletcher et al., 2014a). Multiple infections per cell have been reported in cell-to-cell spread of HIV (Baxter et al., 2014; Boullé et al., 2016; Dang et al., 2004; Del Portillo et al., 2011; Dixit and Perelson, 2004; Duncan et al., 2013; Law et al., 2016; Reh et al., 2015; Russell et al., 2013; Sigal et al., 2011; Zhong et al., 2013). In this mode of HIV transmission, an interaction between the infected donor cell and the uninfected target results in directed transmission of large numbers of virions (Baxter et al., 2014; Groppelli et al., 2015; Hübner et al., 2009; Sowinski et al., 2008). This is in contrast to cell-free infection, where free-floating virus finds target cells through diffusion. Both modes occur simultaneously when infected donor cells are cocultured with targets. However, the cell-to-cell route is thought to be the main cause of multiple infections per cell (Hübner et al., 2009). In the lymph nodes, several studies showed multiple infections (Gratton et al., 2000; Jung et al., 2002; Law et al., 2016) while another study did not (Josefsson et al., 2013). One explanation for the divergent results is that different cell subsets are infected to different degrees. For example, T cells were shown not to be multiply infected in the peripheral blood compartment (Josefsson et al., 2011). However, more recent work investigating markers associated with HIV latency in the face of ART found that the average number of HIV DNA copies per cell is greater than one in 3 out of 12 individuals. This occurred in the face of ART in the CD3-positive, CD32a high CD4 T-cell subset (Descours et al., 2017). In the absence of suppressive ART, it would be expected that the number of HIV DNA copies per cell would be higher. Multiple viral integration attempts per cell may increase the probability of death. One consequence of HIV-mediated death may be that attenuation of infection may increase viral replication by increasing the number of live targets. Indeed, it has been suggested that more attenuated HIV strains result in more successful infections in terms of the ability of the virus to replicate in the infected individual (Ariën et al., 2005; Nowak and May, 2000; Payne et al., 2014; Quiñones-Mateu and Arts, 2006; Wodarz and Levy, 2007). Here, we experimentally examined the effect of attenuating cell-to-cell spread by using HIV inhibitors. We observed that partially inhibiting infection with drug or antibody resulted in an increase in the number of live infected cells in both a cell line and in lymph node cells. This is, to our knowledge, the first experimental demonstration at the cellular level that attenuation of HIV infection can result in an increase in live infected cells under specific infection conditions. Results We introduce a model of infection where each donor to target transmission leads to an infection probability r and death probability q per infection attempt. In our experimental system, one infection attempt is measured as one HIV DNA copy, whether integrated or unintegrated. The probability of successful infection of a target cell given n infection attempts is 1-(1 r)n (Sigal et al., 2011). We define Ln as the probability of a cell to survive infection in the face of n infection attempts. Assuming infection attempts act independently, Ln=(1-q)n. The probability of a cell to be infected and not die after it has been exposed to n infection attempts is therefore: Pn=(1−(1−r)n)(1−q)n This model makes several simplifying assumptions: (1) all infection attempts have equal probabilities to infect targets. (2) The probability for a cell to die from each transmission is equal between transmissions. (3) Infection attempts act independently, and productive infection and death are independent events. In this model, r and q capture the probabilities for a cell to be infected or die post-reverse transcription. For example, mutations which reduce viral fitness by decreasing the probability of HIV to integrate would reduce r, while mutations which reduce the probability of successful reverse transcription would reduce n. If the number of infection attempts n is Poisson distributed with mean λ, the probability for a cell to be infected is 1-e-rλ and the probability of a cell to live is Ln = e-qλ (see Supplementary file 1 for parameters and definitions). As derived in Appendix 1, the probability that a cell is productively infected will be: Pλ=e-λq(1-e-λr(1-q)) Since antiretroviral drugs lead to a reduction in the number of infection attempts by, for example, decreasing the probability of reverse transcription in the case of reverse transcriptase inhibitors, we introduced a drug strength value d, where d = 1 in the absence of drug and d > 1 in the presence of drug. In the presence of drug, λ is decreased to λ/d. The drug therefore tunes λ, and if the antiretroviral regimen is fully suppressive, λ/d is expected to be below what is required for ongoing replication. The probability of a cell to be infected and live given drug strength d is therefore: Pλ/d=e-λq/d(1-e-λr(1-q)/d) Analysis of the probability of a cell to survive and be infected as a function of r and q shows that at each drug strength d/λ, Pλ increases as the probability of infection r increases (Figure 1). Hence, the value of r strongly influences the amplitude of Pλ. How Pλ behaves when drug strength d/λ increases depends on the parameter values of r and q. A subset of parameter values results in a peak in the number of infected cells at intermediate d/λ, decreasing as drug strength increases further (Figure 1). We refer to such a peak in infected numbers as an infection optimum. As q increases, the cost of multiple infections per cell increases, and the infection optimum shifts to higher d/λ values. A fall from the infection optimum at decreasing d/λ is driven by increasing cell death as a result of increasing infection attempts per cell. This slope is therefore shallower, and peaks broader, at low q values (Figure 1). Figure 1 Download asset Open asset Probability for a cell to be infected and live as a function of inhibitor. Probability for a cell to be infected and live was calculated for 20 infection attempts (λ) and represented as a heat map. Drug strength (d/λ) is on the y-axis, and the probability per infection attempt to infect (r) is on the x-axis. Each plot is the calculation for one value of the probability per infection attempt to die (q) denoted in white in the top left corner. https://doi.org/10.7554/eLife.30134.003 Our model assumes that cellular infection and death due to an HIV infection attempt are independent processes. This is based on observations that support a role for cell death as a cellular defence mechanism which may occur before productive infection, such as programmed cell death triggered by HIV integration induced DNA damage (Cooper et al., 2013). An alternative model is that HIV-mediated cell death depends on productive infection. This would be consistent with cell death due to, for example, expression of viral proteins (Westendorp et al., 1995b1995). Since the concentration of viral proteins may also scale with the number of infections per cell, we derived the mathematical model for such a process in the supplementary mathematical analysis. The models are equivalent, showing that independence of cell death and infection is not a necessary condition for an infection optimum to occur in the presence of inhibitor. Given that an infection optimum is dependent on parameter values, we next examined whether these parameter values occur experimentally in HIV infection. We therefore first tested for an infection optimum in the RevCEM cell line engineered to express GFP upon HIV Rev protein expression (Wu et al., 2007). We subcloned the cell line to maximize the frequency of GFP-positive cells upon infection (Boullé et al., 2016). We needed to detect the number of infection attempts per cell λ. To estimate this, we used PCR to detect the number of reverse transcribed copies of viral DNA in the cell by splitting each individual infected cell over multiple wells. We then detected the number of wells with HIV DNA by PCR amplification of the reverse transcriptase gene. Hence, the number of positive wells indicated the minimum number of viral DNA copies per cell, since more than one copy can be contained within the same well (Josefsson et al., 2011; Josefsson et al., 2013). We first measured the number of viral DNA copies in ACH-2 cells, reported to contain a single inactive HIV integration per genome (Chun et al., 1997; O'Doherty et al., 2002). We sorted a total of 166 ACH-2 cells at one cell per well into lysis buffer and subdivided single-cell lysates into four wells (Figure 2—figure supplement 1A). About one quarter of cells showed a PCR product of the expected size. Cells with more than one HIV copy per cell were very rare and may reflect either errors in cell sorting or dividing cells (Figure 2—figure supplement 1B). Similar frequencies were obtained when the ACH-2 cell line was subcloned or split over 10 wells (Figure 2—figure supplement 1C). Given that each ACH-2 cell contains one HIV DNA copy, the frequency of detection indicated our detection efficiency per HIV DNA copy. To investigate the effect of multiple infection attempts per cell, we used coculture infection, where infected (donor) cells are co-incubated with uninfected (target) cells and lead to cell-to-cell spread. We used approximately 2% infected donor cells as our input, and detected the number of HIV DNA copies per cell by flow cytometric sorting of individual GFP-positive cells followed by splitting each cell lysate over 10 wells. Wells were then amplified by PCR and visualized on an agarose gel (Figure 2A). We assayed 60 cells and obtained a wide distribution of viral DNA copies per cell, which ranged from 0 to 9 copies (Figure 2B). The range of HIV DNA copies per cell fit a Poisson distribution with two means better than either a single mean Gaussian or Poisson distribution. However, the fit of the two mean Poisson distribution did not show two obvious peaks, and instead seemed to fit the data better due to the addition of fit parameters (Figure 2—figure supplement 2). Hence we cannot conclude that the distribution is bimodal. We also detected the HIV copy number in 30 GFP-positive cells infected by cell-free HIV. HIV in cell-free form was obtained by filtering supernatant from HIV producing cells to exclude cells or cell fragments, then infecting target cells with the filtered virus. Infection with this virus is defined here as cell-free infection. In this case, we detected either zero or one HIV copy per cell (Figure 2B inset). The frequency of single HIV DNA copies was 0.23, identical to the measured result in the ACH-2 cell line. We computationally corrected the detected number of DNA copies in coculture infection for the sensitivity of our PCR reaction as determined by the ACH-2 results (Materials and methods). On average we obtained 15 ± 3 copies per cell after correction. Figure 2 with 6 supplements see all Download asset Open asset Partial inhibition increases the number of live infected cells. (A) To quantify HIV DNA copy number per cell, GFP-positive cells were sorted into individual wells and lysed. Each lysate was subdivided into 10 wells and PCR performed to detect HIV DNA, with the sum of positive wells being the raw HIV copy number for that cell. (B) Histogram of raw HIV DNA copies per cell in coculture infection (n = 60 cells, four independent experiments). Inset shows raw HIV DNA copies per cell in cell-free infection (n = 30, two independent experiments) (C) Number of live infected cells normalized by maximum number of live infected cells in cell-free infection with EFV. Black line is best-fit for EFV suppression of cell-free infection (IC50 = 2.9 nM, h = 2.1). Means and standard errors for three independent experiments. (D) Number of live infected cells/ml 2 days post-infection resulting from coculture infection of 106 cells/ml in the presence of EFV. Means and standard errors for three independent experiments. Black line is best-fit of Equation (3) with r = 0.22 and q = 0.17. Dashed green line is the result of Equation (3) with experimentally measured r = 0.28, q = 0.15. https://doi.org/10.7554/eLife.30134.004 To tune λ, we added the HIV reverse transcriptase inhibitor efavirenz (EFV) to infections. To calculate d, we used cell-free infection (Figure 2C, see Figure 2—figure supplement 3 for logarithmic y-axis plot), which as verified above, results in single HIV copies per cell. For cell-free infection, we approximate d = 1/Tx, where Tx is defined as the number of infected cells with drug divided by the number of infected cells without drug with single infection attempts (see Materials and methods and [(Sigal et al., 2011]). This is equivalent to 1-ε in a commonly used model describing the effect of inhibitors on infection. In this model, ε is drug effectiveness, with the 50% inhibitory drug concentration (IC50) and the Hill coefficient for drug action as parameter values (Canini and Perelson, 2014; Shen et al., 2008). We fit the observed response of infection to EFV using this approach to estimate d across a range of EFV concentrations. Fit of the model to the cell-free data using wild type, EFV-sensitive HIV showed a monotonic decrease with IC50 = 2.9 nM and Hill coefficient of 2.1 (Figure 2C, black line). We next dialed in EFV to tune λ/d in coculture infection. To obtain the number of infected target cells, and specifically exclude donor cells or donor-target cell fusions, target cells were marked by the expression of mCherry. Donor cells were stained with the vital stain Cell Trace Far Red (CTFR). The concentration of live infected cells was determined after 2 days in coculture with infected donors. Live infected cells were identified based on the absence of cell death indicator dye DAPI fluorescence, and presence of GFP. The input of infected donor cells was excluded from the count of infected cells based on the absence of mCherry fluorescence. Donor-target cell fusions were excluded by excluding CTFR-positive cells (see Figure 2—figure supplement 4 for gating strategy). While the percent of infected cells was reduced with drug, the concentration of live infected cells increased (Figure 2—figure supplement 4). We observed a peak in the number of live infected target cells at 4 nM EFV (Figure 2D). We then fit the number of live infected cells using Equation (3), where Pλ was multiplied by the input number of target cells per ml (106 cells/ml) to obtain the predicted number of live infected cells per ml of culture. This was done to constrain r in the model, which strongly determines the amplitude of Pλ/d as described above. Equation (3) best fit the behaviour of infection when r = 0.22 and q = 0.17, resulting in a peak at 4.8 nM EFV (Figure 2D, black line). Hence an infection optimum is present in the cell line infection system. In order to determine whether the fitted r and q values were within a reasonable range, we measured these values experimentally. To measure r, we infected with cell-free HIV to avoid the broad distribution of HIV copy numbers observed in cell-to-cell spread, and determined the fraction of live infected cells Pλ (Figure 2—figure supplement 5A). We then determined the mean number of HIV copies per cell λ for the same set of experiments corrected by the efficiency of detection (Figure 2—figure supplement 5B). The parameter r was calculated as -ln(1-Pλ)/λ (Supplementary file 2). To measure q, we blocked cell division using serum starvation to measure differences in cell concentration due to cell death only, and not due to proliferation of uninfected cells (Figure 2—figure supplement 5C). We then infected with cell-free HIV and measured Lλ, defined as the fraction of live cells remaining upon infection with λ HIV DNA copies relative to infection blocked with EFV (see below). To specifically detect the decrease in live cells as a result of events downstream of reverse transcription, we compared infected cells to cells exposed to the same virus concentration but treated with 40 nM EFV, a drug concentration where infection by cell-free virus is negligible (Figure 2—figure supplement 3). q was then calculated as -ln(Lλ)/λ, where Lλ was the probability of a cell to live given transmission with λ copies (Supplementary file 2). Measured r and q values were 0.28 ± 0.08 and 0.15 ± 0.07 (mean ±standard deviation), respectively. The solution to Equation (3) using these values showed similar behavior to the solution with the fitted values for wild-type HIV infection, indicating that the fitted values gave a reasonable approximation of the behavior of the system (Figure 2D, dashed green line). In order to investigate the dynamics of cell depletion due to cell-to-cell HIV spread and its modulation by the addition of an inhibitor, we performed time-lapse microscopy over a two day infection window. While infection parameters were different due to the constraints of visualizing cells (Materials and methods), the general trend from the data was deterioration in the number of live cells in the time-lapse culture starting at 1 day post-infection when no drug was added. The deterioration in live cell numbers was averted by the addition of EFV (Figure 2—figure supplement 6). We next investigated whether an infection optimum occurs with EFV-resistant HIV. To derive the resistant mutant, we cultured wild-type HIV in our reporter cell line in the presence of EFV. We obtained the L100I partially resistant mutant. We then replaced the reverse transcriptase of the wild-type molecular clone with the mutant reverse transcriptase gene (Materials and methods). We derived dmut for the L100I mutant using cell-free mutant infection (Figure 3A, see Figure 3—figure supplement 1 for logarithmic y-axis plot). The L100I mutant was found to have an IC50 = 29 nM EFV and a Hill coefficient of 2.0 (Figure 3A, black line). Figure 3 with 2 supplements see all Download asset Open asset Partial inhibition of the EFV-resistant L100I mutant shifts the peak of live infected cells to higher EFV concentrations. (A) The number of live infected cells normalized by the maximum number of live infected cells in cell-free infection as a function of EFV for the L100I mutant. Black line is best-fit for EFV suppression of cell-free infection (IC50 = 29 nM, h = 2.0). Shown are means and standard errors for three independent experiments. (B) Number of live infected cells/ml 2 days post-infection resulting from coculture infection of 106 cells/ml in the presence of EFV. Means and standard errors for three independent experiments. Black line is best-fit of Equation (3) with r = 0.29 and q = 0.13. Dashed green line is the result of Equation (3) with the experimentally measured r = 0.28 and q = 0.15 for wild-type HIV infection. https://doi.org/10.7554/eLife.30134.011 We next performed coculture infection (see Figure 3—figure supplement 2 for gating strategy). Similarly to wild-type HIV coculture infection, there was a peak in the number of live infected target cells for the L100I mutant infection. However, the peak in live infected cells was shifted to 40 nM EFV (Figure 3B). Fits were obtained to Equation (3) using dmut values and λ measured for wild-type infection. The fits recapitulated the experimental results when r = 0.29 and q = 0.13, with a fitted peak at 45 nM EFV (Figure 3B, black line). The solution to Equation (3) using the measured values for r and q showed a similar pattern to that obtained with the fitted values (Figure 3B, dashed green line). We note that both wild type and mutant coculture infection has data points above the fit line at the highest drug concentrations. This may be a limitation of our model at drug values much higher than observed at the infection optimum. In this range of drug values, our model predicts a more pronounced decline in the number of infected cells than is observed experimentally. In order to examine whether a peak in live infected targets can be obtained with an unrelated inhibitor, we used the HIV neutralizing antibody b12. This antibody is effective against cell-to-cell spread of HIV (Baxter et al., 2014; Reh et al., 2015). We obtained a peak in live infected cells at 5 ug/ml b12 (Figure 4). The b12 concentration that resulted in a peak number of live infected cells was the same for wild-type virus and the L100I mutant, showing that L100I mutant fitness gain was EFV specific. In contrast, cell-free infection in the face of b12 showed a sharp and monotonic drop in live infected cells for both wild type and mutant virus (Figure 4—figure supplement 1). Figure 4 with 1 supplement see all Download asset Open asset Partial inhibition of coculture infection with neutralizing antibody results in higher numbers of live infected cells. Shown are the numbers of live infected cells normalized by the maximum number of live infected cells in coculture infection as a function of b12 antibody concentration. Infection was by either EFV-sensitive HIV (blue) or the L100I EFV-resistant mutant (green). Dashed lines are a guide to the eye. Shown are means and standard errors for three independent experiments. https://doi.org/10.7554/eLife.30134.014 While the RevCEM cell line is a useful tool to illustrate the principles governing the formation of an infection optimum, the sensitivity of such an optimum to parameter values would make its presence in primary HIV target cells speculative. We therefore investigated whether a fitness optimum occurs in primary human lymph node cells, the anatomical site which would be most likely to have a high force of infection. We derived human lymph nodes from HIV-negative individuals from indicated lung resections (Supplementary file 3), cellularized the lymph node tissue using mechanical separation, and infected the resulting lymph node cells with HIV. A fraction of the cells was infected by cell-free virus and used as infected donor cells. We added these to uninfected target cells from the same lymph node to test coculture infection, and detected the number of live infected cells 4 days post-infection with the L100I EFV-resistant mutant in the face of EFV. We detected the number of live infected cells by the exclusion of dead cells with the fixable death detection dye eFluor660 followed by single cell staining for HIV Gag using anti-p24 antibody (Figure 5A). Figure 5 with 2 supplements see all Download asset Open asset Infection optimum with EFV in lymph node cells. (A) Number of live infected cells as a function of EFV. Each row shows in vitro infected lymph node cells from one participant. Left column is the number of live infected cells normalized by maximum number of live infected cells in coculture infection. Middle and right columns are flow cytometry dot plots of infection without drug and at the infection optimum, with HIV p24 on x-axis and death detection by eFluor660 on y-axis. Infected live cells are bottom right. Number in brackets represents live infected cell density p
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