scholarly journals A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

2020 ◽  
Author(s):  
Jesse Kreger ◽  
Natalia L. Komarova ◽  
Dominik Wodarz
Keyword(s):  
Synaptic Transmission ◽  
Hiv Infection ◽  
Single Cell ◽  
Virus Transmission ◽  
Cell Interaction ◽  
Deterministic Algorithm ◽  
Cell Subpopulation ◽  
Multiple Infection ◽  
Deterministic Models ◽  
Multiple Copies

AbstractMultiple infection (when a single cell can become super-infected with multiple copies of virus) can effect evolutionary processes in HIV infection, such as the generation and spread of different mutations. Synaptic transmission (where multiple copies of virus can be transferred during a single cell-to-cell interaction) leads to an increase in infection multiplicity. Here, we analyze the effect of multiple infection on the evolution of an HIV infection using a hybrid stochastic-deterministic algorithm. This algorithm allows us to stochastically simulate a large number of cells and large number of cell subpopulations in a computationally efficient way. Specifically, we classify each cell subpopulation as “large” or “small” based on some size threshold (for which we provide an analytical lower bound), and then simulate the small populations stochastically, while simultaneously using the deterministic equations for the large populations. We first demonstrate to what extent deterministic models are incomplete in predicting mutant dynamics, and then use the hybrid method to study aspects of mutant evolution. Focusing on the mutant populations at the time of peak infection, we find that overall, multiple infection promotes the existence of mutant strains of virus, both in terms of faster generation and overall number. It also increases the variance in mutant numbers and results in a larger probability to observe rare strains, such as triple mutants. In the context of multiple infection, synaptic transmission is superior at generating mutants, as it increases the number of transcription events that occur. This effect is observed for neutral and advantageous mutants, even in the presence of interference. For disadvantageous mutants, purely synaptic or purely free virus transmission pathways result in an increased number of mutants, but in the presence of complementation, purely synaptic transmission maximizes mutant production and spread.

scholarly journals A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

2021 ◽  
Vol 17 (12) ◽  
pp. e1009713
Author(s):  
Jesse Kreger ◽  
Natalia L. Komarova ◽  
Dominik Wodarz
Keyword(s):  
Infected Cell ◽  
Population Size ◽  
Cell Population ◽  
Stochastic Dynamics ◽  
Heterogeneous Systems ◽  
Deterministic Algorithm ◽  
Stochastic Simulations ◽  
Computational Time ◽  
Multiple Infection ◽  
Deterministic Models

To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability.

scholarly journals EPEN-22. SINGLE-CELL RNA SEQUENCING IDENTIFIES UPREGULATION OF IKZF1 IN PFA2 MYELOID SUBPOPULATION DRIVING AN ANTI-TUMOR PHENOTYPE

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii312-iii312
Author(s):  
Andrea Griesinger ◽  
Eric Prince ◽  
Andrew Donson ◽  
Kent Riemondy ◽  
Timothy Ritzman ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Activation ◽  
Expression Profiles ◽  
Myeloid Cells ◽  
Cell Subset ◽  
Lineage Tracing ◽  
Machine Learning Techniques ◽  
Cell Subpopulation ◽  
Immune Gene

Abstract We have previously shown immune gene phenotype variations between posterior fossa ependymoma subgroups. PFA1 tumors chronically secrete IL-6, which pushes the infiltrating myeloid cells to an immune suppressive function. In contrast, PFA2 tumors have a more immune activated phenotype and have a better prognosis. The objective of this study was to use single-cell(sc) RNAseq to descriptively characterize the infiltrating myeloid cells. We analyzed approximately 8500 cells from 21 PFA patient samples and used advanced machine learning techniques to identify distinct myeloid and lymphoid subpopulations. The myeloid compartment was difficult to interrupt as the data shows a continuum of gene expression profiles exist within PFA1 and PFA2. Through lineage tracing, we were able to tease out that PFA2 myeloid cells expressed more genes associated with an anti-viral response (MHC II, TNF-a, interferon-gamma signaling); while PFA1 myeloid cells had genes associated with an immune suppressive phenotype (angiogenesis, wound healing, IL-10). Specifically, we found expression of IKZF1 was upregulated in PFA2 myeloid cells. IKZF1 regulates differentiation of myeloid cells toward M1 or M2 phenotype through upregulation of either IRF5 or IRF4 respectively. IRF5 expression correlated with IKZF1, being predominately expressed in the PFA2 myeloid cell subset. IKZF1 is also involved in T-cell activation. While we have not completed our characterization of the T-cell subpopulation, we did find significantly more T-cell infiltration in PFA2 than PFA1. Moving forward these studies will provide us with valuable information regarding the molecular switches involved in the tumor-immune microenvironment and to better develop immunotherapy for PFA ependymoma.

scholarly journals The structure of acute chemical poisoning in patients with HIV infection in the emergency hospital

2018 ◽  
pp. 3-13
Author(s):  
М.М. Поцхверия ◽  
М.В. Белова ◽  
С.А. Солонин ◽  
М.А. Годков
Keyword(s):  
Hiv Infection ◽  
Virus Transmission ◽  
Pairwise Comparison ◽  
Simple Random Sampling ◽  
Immunoblot Analysis ◽  
Acute Poisoning ◽  
Economic Problem ◽  
High Risk Group ◽  
Emergency Hospital ◽  
Number Of Patients

Употребление психоактивных веществ (ПАВ) с немедицинскими целями является огромной медико-социальной, экономической проблемой, и становится наиболее частой причиной инфицирования ВИЧ. Наркозависимые лица представляют особо уязвимую группу для заражения. Цель исследования: изучить структуру веществ, вызвавших острые отравления у ВИЧ-инфицированных пациентов, госпитализированных в стационар скорой медицинской помощи. Пациенты и методы исследования: ретроспективно проанализированы структура острых отравлений химической этиологии (ООХЭ) и результаты освидетельствования на ВИЧ-инфекцию 19 061 пациента, госпитализированных (простая случайная выборка) в отделение лечения острых отравлений НИИ СП им. Н.В. Склифосовского (ОЛОО НИИ СП) в 2013-2016 гг. Диагноз ООХЭ верифицирован методом хромато-масс-спектрометрии. Диагностику ВИЧ-инфекции осуществляли с использованием иммуноферментного анализа и иммуноблотинга. Для попарного сравнения распределения частот выявляемости ВИЧ у лиц ООХЭ использовали точный тест Фишера. Различия оценивали как статистически значимые при p<0,05. Результаты исследования. Выявляемость ВИЧ-инфекции у пациентов с ООХЭ варьировала от 5,7 до 7,7%. Среди пациентов с ВИЧ подавляющее большинство обращений было связано с отравлениями опиатами, лекарственными средствами и различными смесями ПАВ. За три года количество пациентов с ВИЧ и передозировками опиатов сократилось в 2,3 раза (p<0,0001). Значительно чаще стали встречаться отравления психодислептиками, смесями ПАВ и веществами немедицинского назначения. Среди ВИЧ-инфицированных значительную долю составляли лица с отравлениями несколькими видами наркотических и/или лекарственных веществ. При этом снизились доли отравлений опиатами в сочетании с метадоном и психофармакологическими средствами. У пациентов с ВИЧ обнаружен высокий удельный вес интоксикации этанолом и его суррогатами. Выводы. Динамика выявляемости ВИЧ-инфекции у пациентов с ООХЭ свидетельствует о высокой поражённости этой категории лиц. Причины увеличения частоты обнаружения ВИЧ-инфекции могут быть связаны с изменением ассортимента принимаемых ПАВ и путей передачи вируса. Пациенты ОЛОО являются группой высокого риска распространения ВИЧ-инфекции и могут рассматриваться как фокусная группа, отражающая общие тенденции в потреблении наркотических и ПАВ в г. Москве. The use of psychoactive substances (PS) with non-medical purposes is a huge medical, social and economic problem. It becomes the most frequent cause of HIV infection. Drug addict individuals are vulnerable group for HIV. Aim: study the structure of substances that caused acute poisoning in HIV-infected patients hospitalized in an emergency hospital. The object and methods: it has been retrospectively analyzed the structure of acute poisoning of chemical etiology (APCE) and prevalence of HIV infection among 19061 patients hospitalized (simple random sampling) at N.V. Sklifosovsky’s Research Institute for Emergency Medicine department of acute poisoning treatment (DAPT) in 2013-2016 years. The diagnosis of APCE was verified by chromatography-mass spectrometry. Diagnosis of HIV infection was carried out using immunoassay and immunoblot analysis. Fisher’s exact test was used for a pairwise comparison of the prevalence HIV in individuals with APCE. Differences were assessed as statistically significant at p <0.05. Results. The prevalence of HIV infection in patients with APCE ranged from 5.7 to 7.7%. Among the patients with HIV the vast majority of cases were associated with poisoning with opiates, drugs and various PS mixtures. For the period from 2013 to 2016 the number of patients with HIV infection and opiate overdoses decreased by 2.3 times (p <0.0001). More common became poisoning with psychotomimetic substance, PS mixtures and non-medical substances. Among HIV infected patients significant share were people poisoned several types of drugs and/or medicines. At the same time, the share of poisoning with opiates decreased in combination with methadone and psychopharmacological medicines. In patients with HIV were detected a high proportion of ethanol and its surrogates’ intoxication. Conclusions. The prevalence of HIV infection in patients with APSE indicates of the high affection of this category persons. The reasons of increasing the prevalence of HIV infection can be associated with a change in PS consumption assortment and the ways of virus transmission. Patients with APSE are a high risk group for HIV spreading and can be considered as a focus group reflecting general trends in the drug consumption in Moscow.

scholarly journals Single-cell characterization and quantification of translation-competent viral reservoirs in treated and untreated HIV infection

2019 ◽  
Vol 15 (2) ◽  
pp. e1007619 ◽  
Author(s):  
Marion Pardons ◽  
Amy E. Baxter ◽  
Marta Massanella ◽  
Amélie Pagliuzza ◽  
Rémi Fromentin ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Single Cell ◽  
Viral Reservoirs ◽  
Cell Characterization

Single-Cell Analysis Reveals Heterogeneity of Virus Infection, Pathogenicity, and Host Responses: HIV as a Pioneering Example

2020 ◽  
Vol 7 (1) ◽  
pp. 333-350
Author(s):  
Ludivine Brandt ◽  
Sara Cristinelli ◽  
Angela Ciuffi
Keyword(s):  
Hiv Infection ◽  
Single Cell ◽  
Viral Infections ◽  
Single Cell Analysis ◽  
Host Responses ◽  
Cell Analysis ◽  
Host Interactions ◽  
Immunodeficiency Virus ◽  
Extreme Phenotypes ◽  
Individual Consideration

While analyses of cell populations provide averaged information about viral infections, single-cell analyses offer individual consideration, thereby revealing a broad spectrum of diversity as well as identifying extreme phenotypes that can be exploited to further understand the complex virus-host interplay. Single-cell technologies applied in the context of human immunodeficiency virus (HIV) infection proved to be valuable tools to help uncover specific biomarkers as well as novel candidate players in virus-host interactions. This review aims at providing an updated overview of single-cell analyses in the field of HIV and acquired knowledge on HIV infection, latency, and host response. Although HIV is a pioneering example, similar single-cell approaches have proven to be valuable for elucidating the behavior and virus-host interplay in a range of other viruses.

scholarly journals Computation of Single-Cell Metabolite Distributions Using Mixture Models

2020 ◽  
Vol 8 ◽  
Author(s):  
Mona K. Tonn ◽  
Philipp Thomas ◽  
Mauricio Barahona ◽  
Diego A. Oyarzún
Keyword(s):  
Single Cell ◽  
Mixture Models ◽  
Single Cells ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Stochastic Simulations ◽  
Deterministic Models ◽  
Metabolic Heterogeneity ◽  
Cell Expression ◽  
The Impact

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

scholarly journals SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies

2019 ◽  
Author(s):  
Srikanth Ravichandran ◽  
András Hartmann ◽  
Antonio del Sol
Keyword(s):  
Single Cell ◽  
Control Cell ◽  
General Purpose ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Cell Subpopulation ◽  
Transcriptional Networks ◽  
Data Generation ◽  
Computational Tool ◽  
Precise Control

Abstract Summary Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional networks. Targeted perturbation of these signaling hotspots can enable precise control of cell subpopulation phenotypes. SigHotSpotter correctly predicts the signaling hotspots with known experimental validations in different cellular systems. The tool is simple, user-friendly and is available as web-server or as stand-alone software. We believe SigHotSpotter will serve as a general purpose tool for the systematic prediction of signaling hotspots based on single-cell RNA-seq data, and potentiate novel cell rejuvenation strategies in the context of disease and ageing. Availability and implementation SigHotSpotter is at https://SigHotSpotter.lcsb.uni.lu as a web tool. Source code, example datasets and other information are available at https://gitlab.com/srikanth.ravichandran/sighotspotter. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals rCASC: reproducible classification analysis of single-cell sequencing data

GigaScience ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Luca Alessandrì ◽  
Francesca Cordero ◽  
Marco Beccuti ◽  
Maddalena Arigoni ◽  
Martina Olivero ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Heterogeneity ◽  
Cell Subpopulation ◽  
Integrated Analysis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Cell Stability ◽  
Reproducible Analysis

Abstract Background Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g., ATAC-seq. Many related algorithms and tools have been developed, but few computational workflows provide analysis flexibility while also achieving functional (i.e., information about the data and the tools used are saved as metadata) and computational reproducibility (i.e., a real image of the computational environment used to generate the data is stored) through a user-friendly environment. Findings rCASC is a modular workflow providing an integrated analysis environment (from count generation to cell subpopulation identification) exploiting Docker containerization to achieve both functional and computational reproducibility in data analysis. Hence, rCASC provides preprocessing tools to remove low-quality cells and/or specific bias, e.g., cell cycle. Subpopulation discovery can instead be achieved using different clustering techniques based on different distance metrics. Cluster quality is then estimated through the new metric "cell stability score" (CSS), which describes the stability of a cell in a cluster as a consequence of a perturbation induced by removing a random set of cells from the cell population. CSS provides better cluster robustness information than the silhouette metric. Moreover, rCASC's tools can identify cluster-specific gene signatures. Conclusions rCASC is a modular workflow with new features that could help researchers define cell subpopulations and detect subpopulation-specific markers. It uses Docker for ease of installation and to achieve a computation-reproducible analysis. A Java GUI is provided to welcome users without computational skills in R.

scholarly journals scDoc: correcting drop-out events in single-cell RNA-seq data

2020 ◽  
Vol 36 (15) ◽  
pp. 4233-4239
Author(s):  
Di Ran ◽  
Shanshan Zhang ◽  
Nicholas Lytal ◽  
Lingling An
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Drop Out ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Cell Subpopulation ◽  
Rna Seq ◽  
Imputation Methods ◽  
Similarity Estimation ◽  
Differential Expression Detection

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of ‘drop-out’ events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. Results scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. Availability and implementation R code is available at https://github.com/anlingUA/scDoc. Supplementary information Supplementary data are available at Bioinformatics online.

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