Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation to analyze extracellular viral markers.

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

Anti-HTLV-1 immunity combined with proviral load as predictive biomarkers for adult T-cell leukemia-lymphoma.

Human T-cell leukemia virus type 1 (HTLV-1) establishes chronic infection in humans and induces a T-cell malignancy called adult T-cell leukemia-lymphoma (ATL) and several inflammatory diseases such as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Persistent HTLV-1 infection is established under the pressure of host immunity, and therefore the immune response against HTLV-1 is thought to reflect the status of the disease it causes. Indeed, it is known that cellular immunity against viral antigens is suppressed in ATL patients compared to HAM/TSP patients. In this study, we show that profiling the humoral immunity to several HTLV-1 antigens, such as Gag, Env, and Tax, and measuring proviral load are useful tools for classifying disease status and predicting disease development. Using targeted sequencing, we found that several carriers whom this profiling method predicted to be at high risk for developing ATL indeed harbored driver mutations of ATL. The clonality of HTLV-1-infected cells in those carriers was still polyclonal; it is consistent with an early stage of leukemogenesis. Furthermore, this study revealed significance of anti-Gag proteins to predict high risk group in HTLV-1 carriers. Consistent with this finding, anti-Gag cytotoxic T lymphocytes (CTLs) were increased in patients who received hematopoietic stem cell transplantation and achieved remission state, indicating the significance of anti-Gag CTLs for disease control. Our findings suggest that our strategy that combines anti-HTLV-1 antibodies and proviral load may be useful for prediction of the development of HTLV-1-associated diseases.

Disentangling the growth curve of microbial culture.

Many researchers have studied the population dynamics of microbe of microbes as a typical example of population dynamics. The Monod equation, which mainly focuses on the growth and stationary phases, is used when plotting a growth curve. However, the growth potential in the late stage of culture has been overlooked. Previous studies considered the direct degradation of products to the limiting substrate. In this study, we considered microbial growth during the stationary phase, which enables us to describe the dynamics precisely. The microbes were divided into two populations: one grew by consuming the limiting substrate and the other degraded the products by metabolism. According to the numerical analysis of our model, microbes may choose one of two strategies: one consumes substrates and expands quickly, and the other grows slowly while cleaning up the environment in which they thrive. Furthermore, we found three types of microbial growth depending on their ability to detect metabolite accumulation. Using experimentally measured data, this model can estimate the dynamics of cell density, the substrates, and the metabolites used. The model's disentangling of growth curves offers novel interpretive possibilities for culture system dynamics.

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation for analyzing extracellular viral markers.

Chronic infection of hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. The quantifying and understanding dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, it requires a liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because of the ethical aspect. We here aimed to develop a non-invasive method for quantifying cccDNA in the liver using surrogate markers present in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations (PDEs), integrates experimental data from in vitro and in vivo investigations. By applying this model, we successfully predicted the amount and dynamics of intrahepatic cccDNA using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The non-invasive quantification of cccDNA using our proposed methodology holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

Comparison of RNA-Seq analysis data between tracheal mite-infested and uninfested Japanese honey bees (Apis cerana japonica).

OBJECTIVE: The purpose of this data set is to investigate differences in RNA-Seq transcriptome profiles between Acarapis woodi-infested and uninfested Japanese honey bees (Apis cerana japonica). The data set is strengthened by data collected from different body parts (head, thorax, and abdomen). The data set will support future studies of molecular biological changes in mite-infested honey bees. DATA DESCRIPTION: We collected 5 mite-infested and 5 uninfested A. cerana japonica workers from each of 3 different colonies (designated as A, B, and C). Workers were dissected into 3 body sites (i.e., heads, thoraces, and abdomen), and 5 of each body site were pooled together for RNA extraction, generating a total of 18 RNA-Seq samples (2 infection status × 3 colonies × 3 body sites). FASTQ data files of each sample that were generated by a DNBSEQ-G400 sequencer with the 2 × 100 bp paired-end sequencing protocol are available in the DDBJ Sequence Read Archive under accession number DRA015087 (RUN: DRR415616-DRR415633, BioProject: PRJDB14726, BioSample: SAMD00554139-SAMD00554156, Experiment: DRX401183-DRX401200). The data set is a fine-scale analysis of gene expression in the mite-infested A. cerana japonica workers because 18 RNA-Seq samples are separated by 3 body sites.

COVID-19-related stress, exercise, and oral health-related quality of life among community-dwelling older adults who participated in the CHEER Iwamizawa project, Japan.

This study examined the association between coronavirus disease 2019 (COVID-19)-related stress, exercise habits, and oral health-related quality of life (OHRQoL) in a sample of 215 community-dwelling older adults in Japan (57 men, 158 women; Mage = 74.2 years, SD = 6.0). Data were collected during wellness checkups in October 2020 and included participants' demographic characteristics, measures of instrumental activities of daily living and depressive tendencies, number of teeth, oral hypofunction, OHRQoL, COVID-19-related stress, and exercise habits. Four mutually exclusive groups were created, using the presence or absence of COVID-19-related stress and lack of exercise habits as risk factors for poor OHRQoL (no COVID-19-related stress and no lack of exercise, COVID-19-related stress only, lack of exercise habits only, and both COVID-19-related stress and lack of exercise habits). Poisson regression with robust standard errors provided the prevalence ratio for poor OHRQoL. The presence of both COVID-19-related stress and lack of exercise habits (adjusted prevalence ratio: 2.20, 95% CI: 1.31- 3.69) was associated with poor OHRQoL. The results indicate that COVID-19-related stress and exercise habits should be considered when designing oral health and public health initiatives.

Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response.

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs.

Analysis of the Pancreatic Cancer Microbiome Using Endoscopic Ultrasound-Guided Fine-Needle Aspiration-Derived Samples.

OBJECTIVES: Most previous studies have analyzed bacteria in tumors using resected pancreatic cancer (PC) tissues, because it is difficult to obtain tissue samples from unresectable advanced PC. We aimed to determine whether minimal tissue obtained by endoscopic ultrasound-guided fine-needle aspiration is useful for microbiome analysis. METHODS: Thirty PC and matched duodenal and stomach tissues (N = 90) were prospectively collected from 30 patients who underwent endoscopic ultrasound-guided fine-needle aspiration. Bacterial DNA was extracted, and 16S rRNA sequencing was performed. The primary outcome was the success rate of bacterial detection in tumors. Bacterial diversity and structure were investigated. RESULTS: The bacterial detection rates were 80%, 100%, and 97% in PC, gastric, and duodenal samples, respectively. Pancreatic cancer tissues showed a lower α-diversity and a significantly different microbial structure than stomach and duodenal tissues. Proteobacteria were more abundant, whereas Firmicutes, Bacteroidetes, and Fusobacteria were less abundant in PC tissues than in stomach and duodenal tissues. Acinetobacter was more abundant in PC tissues than in stomach and duodenal tissues, and Delftia was more frequently detected in resectable PC. CONCLUSIONS: Endoscopic ultrasound-guided fine-needle aspiration samples were valuable for PC microbiome analysis, revealing that the bacterial composition of PC is different from that of the stomach and duodenum.

Evolving neural networks through bio-inspired parent selection in dynamic environments.

Environmental variability often degrades the performance of algorithms designed to capture the global convergence of a given search space. Several approaches have been developed to challenge environmental uncertainty by incorporating biologically inspired notions, focusing on crossover, mutation, and selection. This study proposes a bio-inspired approach called NEAT-HD, which focuses on parent selection based on genetic similarity. The originality of the proposed approach rests on its use of a sigmoid function to accelerate species formation and contribute to population diversity. Experiments on two classic control tasks were performed to demonstrate the performance of the proposed method. The results show that NEAT-HD can dynamically adapt to its environment by forming hybrid individuals originating from genetically distinct parents. Additionally, an increase in diversity within the population was observed due to the formation of hybrids and novel individuals, which has never been observed before. Comparing two tasks, the characteristics of NEAT-HD were improved by appropriately setting the algorithm to include the distribution of genetic distance within the population. Our key finding is the inherent potential of newly formed individuals for robustness against dynamic environments.

Antithetic effect of interferon-α on cell-free and cell-to-cell HIV-1 infection.

In HIV-1-infected individuals, transmitted/founder (TF) virus contributes to establish new infection and expands during the acute phase of infection, while chronic control (CC) virus emerges during the chronic phase of infection. TF viruses are more resistant to interferon-alpha (IFN-α)-mediated antiviral effects than CC virus, however, its virological relevance in infected individuals remains unclear. Here we perform an experimental-mathematical investigation and reveal that IFN-α strongly inhibits cell-to-cell infection by CC virus but only weakly affects that by TF virus. Surprisingly, IFN-α enhances cell-free infection of HIV-1, particularly that of CC virus, in a virus-cell density-dependent manner. We further demonstrate that LY6E, an IFN-stimulated gene, can contribute to the density-dependent enhancement of cell-free HIV-1 infection. Altogether, our findings suggest that the major difference between TF and CC viruses can be explained by their resistance to IFN-α-mediated inhibition of cell-to-cell infection and their sensitivity to IFN-α-mediated enhancement of cell-free infection.

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks.

Network-based assessments are important for disentangling complex microbial and microbial-host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka-Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.

Oral frailty and carriage of oral Candida in community-dwelling older adults (Check-up to discover Health with Energy for senior Residents in Iwamizawa; CHEER Iwamizawa).

OBJECTIVE: To examine the association between oral frailty and oral Candida carriage as a general indicator of deteriorating oral function in older adults. BACKGROUND: Older adults exhibit an elevated risk of oral candidiasis caused by Candida. Although many studies have identified factors associated with oral Candida carriage, none have evaluated its relationship with oral function. MATERIALS AND METHODS: This study included 210 community-dwelling older adults aged ≥60 years who participated in wellness checks. Fungal flora expression in saliva samples was evaluated to identify oral C. albicans and C. glabrata. Participants were categorised by detection of neither strain (group 1), either one of the strains (group 2), or both strains (group 3). The relationship between oral Candida carriage and oral frailty was evaluated by multinomial logistic regression analysis. RESULTS: The participants included 58 men and 152 women with a mean age of 74.2 ± 6.1 years. A total of 88 (41.9%), 94 (44.8%) and 28 (13.3%) participants were assigned to groups 1, 2 and 3 respectively. In the multinomial logistic regression analysis, significant associations were observed between group 1 and group 2 for "Have you choked on your tea or soup recently?" and the number of applicable oral frailty items. Between group 1 and group 3, significant associations were observed for the number of remaining teeth, masticatory performance and the number of applicable oral frailty items. CONCLUSION: We obtained basic data useful for intervention studies aimed at verifying whether oral function management prevents deterioration of the oral bacterial flora.

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study.

BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2.

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

Development of a rapid scabies immunodiagnostic assay based on transcriptomic analysis of Sarcoptes scabiei var. nyctereutis.

Scabies is a highly contagious skin disease caused by the mite Sarcoptes scabiei that affects many mammals. However, the sensitivity of traditional tests for scabies diagnosis in humans is less than 50%. To simplify the diagnosis of scabies, methods that are simple, sensitive, specific, and cost-effective are required. We developed an immunodiagnostic test based on S. scabiei var. nyctereutis RNA-seq data collected from Japanese raccoon dogs with sarcoptic mange. Three candidate antigens-a highly expressed hypothetical protein "QR98_0091190," another mite allergen known as "SMIPP-Cc," and an abundant "vitellogenin-like protein"-were evaluated by western-blot analysis. A lateral flow immunoassay, using specific antibodies against the vitellogenin-like protein, successfully detected scabies in the skin flakes of S. scabiei-infected raccoon dogs. This assay can potentially diagnose scabies more accurately in wildlife, as well as in humans.

Dynamic Associations of Milk Components With the Infant Gut Microbiome and Fecal Metabolites in a Mother-Infant Model by Microbiome, NMR Metabolomic, and Time-Series Clustering Analyses.

Background: The gut microbiome and fecal metabolites of breastfed infants changes during lactation, and are influenced by breast milk components. This study aimed to investigate dynamic associations of milk components with the infant gut microbiome and fecal metabolites throughout the lactation period in a mother-infant model. Methods: One month after delivery, breast milk and subsequent infant feces were collected in a pair for 5 months from a mother and an exclusively breastfed infant. Composition of the fecal microbiome was determined with 16S rRNA sequencing. Low-molecular-weight metabolites, including human milk oligosaccharides (HMOs), and antibacterial proteins were measured in feces and milk using 1H NMR metabolomics and enzyme-linked immunosorbent assays. The association of milk bioactive components with the infant gut microbiome and fecal metabolites was determined with Python clustering and correlation analyses. Results: The HMOs in milk did not fluctuate throughout the lactation period. However, they began to disappear in infant feces at the beginning of month 4. Notably, at this time-point, a bifidobacterium species switching (from B. breve to B. longum subsp. infantis) occurred, accompanied by fluctuations in several metabolites including acetate and butyrate in infant feces. Conclusions: Milk bioactive components, such as HMOs, might play different roles in the exclusively breastfed infants depending on the lactation period.

Modeling Borna Disease Virus In Vitro Spread Reveals the Mode of Antiviral Effect Conferred by an Endogenous Bornavirus-Like Element.

Endogenous retroviruses have demonstrated exaptation during long-term evolution with hosts, e.g., resulting in acquisition of antiviral effect on related extant viral infections. While empirical studies have found that an endogenous bornavirus-like element derived from viral nucleoprotein (itEBLN) in the ground squirrel genome shows antiviral effect on virus replication and de novo infection, the antiviral mechanism, dynamics, and quantitative effect of itEBLN remain unknown. In this study, we experimentally and theoretically investigated the dynamics of how an extant bornavirus, Borna disease virus 1 (BoDV-1), spreads and replicates in uninfected, BoDV-1-infected, and itEBLN-expressing cultured cells. Quantifying antiviral effect based on time course data sets, we found that the antiviral effects of itEBLN are estimated to be 75% and 34% on intercellular virus spread and intracellular virus replication, respectively. This discrepancy between intercellular virus spread and intracellular viral replication suggests that viral processes other than the replication of viral ribonucleoprotein complex (RNP) contributed to the suppression of virus spread in itEBLN-expressing cells. Because itEBLN binds to the BoDV-1 RNP, the suppression of viral RNP trafficking can be an attractive candidate explaining this discrepancy.IMPORTANCE Accumulating evidence suggests that some endogenous viral elements (EVEs), including endogenous retroviruses and endogenous nonretroviral virus elements, have acquired functions in the host as a result of long-term coevolution. Recently, an endogenous bornavirus-like element (itEBLN) found in the ground squirrel genome has been shown to have antiviral activity against exogenous bornavirus infection. In this study, we first quantified bornavirus spread in cultured cells and then calculated the antiviral activity of itEBLN on bornavirus infection. The calculated antiviral activity of itEBLN suggests its suppression of multiple processes in the viral life cycle. To our knowledge, this is the first study quantifying the antiviral activity of EVEs and speculating on a model of how some EVEs have acquired antiviral activity during host-virus arms races.

Should a viral genome stay in the host cell or leave? A quantitative dynamics study of how hepatitis C virus deals with this dilemma.

Virus proliferation involves gene replication inside infected cells and transmission to new target cells. Once positive-strand RNA virus has infected a cell, the viral genome serves as a template for copying ("stay-strategy") or is packaged into a progeny virion that will be released extracellularly ("leave-strategy"). The balance between genome replication and virion release determines virus production and transmission efficacy. The ensuing trade-off has not yet been well characterized. In this study, we use hepatitis C virus (HCV) as a model system to study the balance of the two strategies. Combining viral infection cell culture assays with mathematical modeling, we characterize the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral release characteristics. We found that 0.63% and 1.70% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial proliferation rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leave-strategy provides a higher level of initial transmission for Jc1-n, whereas, in contrast, the stay-strategy provides a higher initial proliferation rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses, which contributes to the efficient control of virus transmission. Ours is the first study to analyze the stay-leave trade-off during the viral life cycle and the significance of the replication-release switching mechanism for viral proliferation.

Multiomics Investigation Revealing the Characteristics of HIV-1-Infected Cells In Vivo.

For eradication of HIV-1 infection, it is important to elucidate the detailed features and heterogeneity of HIV-1-infected cells in vivo. To reveal multiple characteristics of HIV-1-producing cells in vivo, we use a hematopoietic-stem-cell-transplanted humanized mouse model infected with GFP-encoding replication-competent HIV-1. We perform multiomics experiments using recently developed technology to identify the features of HIV-1-infected cells. Genome-wide HIV-1 integration-site analysis reveals that productive HIV-1 infection tends to occur in cells with viral integration into transcriptionally active genomic regions. Bulk transcriptome analysis reveals that a high level of viral mRNA is transcribed in HIV-1-infected cells. Moreover, single-cell transcriptome analysis shows the heterogeneity of HIV-1-infected cells, including CXCL13high cells and a subpopulation with low expression of interferon-stimulated genes, which can contribute to efficient viral spread in vivo. Our findings describe multiple characteristics of HIV-1-producing cells in vivo, which could provide clues for the development of an HIV-1 cure.

The Rules of Human T Cell Fate in vivo.

The processes governing lymphocyte fate (division, differentiation, and death), are typically assumed to be independent of cell age. This assumption has been challenged by a series of elegant studies which clearly show that, for murine cells in vitro, lymphocyte fate is age-dependent and that younger cells (i.e., cells which have recently divided) are less likely to divide or die. Here we investigate whether the same rules determine human T cell fate in vivo. We combined data from in vivo stable isotope labeling in healthy humans with stochastic, agent-based mathematical modeling. We show firstly that the choice of model paradigm has a large impact on parameter estimates obtained using stable isotope labeling i.e., different models fitted to the same data can yield very different estimates of T cell lifespan. Secondly, we found no evidence in humans in vivo to support the model in which younger T cells are less likely to divide or die. This age-dependent model never provided the best description of isotope labeling; this was true for naïve and memory, CD4+ and CD8+ T cells. Furthermore, this age-dependent model also failed to predict an independent data set in which the link between division and death was explored using Annexin V and deuterated glucose. In contrast, the age-independent model provided the best description of both naïve and memory T cell dynamics and was also able to predict the independent dataset.