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Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization. Author summary: Understanding the dynamic changes behind biological processes is important for determining molecular mechanisms underlying normal tissue formulation, developmental disorders and pathologies. Usually, a biological process can be characterized by identifying a trajectory, a path that goes through the various cellular states associated with the process. Since cells in different states may express different sets of genes, researchers often infer cell trajectory via capturing transcriptomics changes. Dozens of methods have been developed for cell trajectory inference, and scRNA-seq data is predominantly utilized. However, cells from different clones may exist distinct differentiation patterns, while methods based only on scRNA-seq data cannot capture this heterogeneity. T cells play a key role in the immune system, and their high antigen recognition specificity is largely determined by their TCR sequences. Thanks to the advent of scTCR-seq technology, we can now identify the group of cells coming from the same clone. This paper describes our novel computational framework, namely LRT, and demonstrates that by complementing scRNA-seq data with the clonal information from scTCR-seq data using LRT, we are able to examine the clonal differentiation heterogeneity that cannot be revealed solely based on scRNA-seq data. [ABSTRACT FROM AUTHOR]

The AIDS is a chronic disease and the researchers still exert their high efforts to reach the cure of HIV infection. The most common treatment is the antiretroviral therapy (cART) and the virus can be more effected if the patients stop using cART. The other problem is that the CD8+ T cells might be exhausted by persistent immune activation by cART. This paper introduces fractional-order into a mathematical model of HIV infection combining with stem cell therapy and control the infection by the immune system cells (CD8+ T cells). The paper introduced the numerical solutions for the mathematical model. The results show that the stem cell therapy with the activation of immune system cells might causes the cure for a HIV patient. This results are consistent with medical studies. Also, we proposed the effect of the fractional order (α) on the figures of the components. [ABSTRACT FROM AUTHOR]

T cells recognize their ligand, the peptide major histocompatibility complex (MHC), via the T-cell receptor (TCR), which is composed of covalently linked α and β or γ and δ chains. This recognition is critical for T-cell ontogeny and controls the selection, activation, and function of T lymphocytes. Specific TCR αβ variable regions have been associated with immunopathogenesis of Chagas disease. Here, we present a systematic review that compiles experimental in vivo and human data regarding the preferential expression of variable alpha (Vα) and variable beta (Vβ) chain regions in Trypanosoma cruzi infection. The original studies indexed in PubMed/Medline, Scopus, and Web of Science databases were screened according to the PRISMA strategy. The analysis showed that expression of TCR Vα subfamilies were evaluated in one human study, and, unlike TCR Vβ, TCR Vα presented a more restricted usage. Despite the great variability in the usage of TCR Vβ regions in human Chagas disease, a down-regulation of TCR Vβ5 expression by T cells from patients in the acute phase of the disease was shown. Opposingly, this TCR region was found overly expressed in CD4+ T cells from chronic Chagas patients. It was also demonstrated that murine Vβ9+ T cells derived from nonlymphoid organs of T. cruzi-infected animals had a modulatory profile, while splenic Vβ9+ T cells produced inflammatory cytokines, indicating that although they display the same TCR Vβ region usage, these cells are functionally distinct. Despite the limitations of few papers and year of publication of the studies, compiling the data derived from them reveals that further investigation of TCR usage will point to their potential role in protective or pathogenic responses, as biomarkers of disease progression, and in the search for dominant peptides potentially useful for the development of vaccines or therapies. Author summary: Chagas disease is a neglected tropical disease, caused by infection with Trypanosoma cruzi. Differential expression of certain T-cell receptor (TCR) variable regions has been associated with the immunopathogenesis of Chagas disease. Here, we present a systematic review that compiled experimental in vivo and human data regarding the preferential expression of TCR alpha and beta chain variable regions in Chagas disease. The original studies indexed in the PubMed/Medline, Scopus, and Web of Science databases were screened according to the PRISMA strategy. Despite the great variability in the use of TCR Vβ in T. cruzi infection, the outcomes indicate that there is a down-regulation of TCR Vβ5 expression in T cells from patients in the acute phase of Chagas disease. However, this region is preferentially expressed by CD4+ T cells from chronic Chagas patients. Additionally, it has been demonstrated that murine Vβ9+ T cells derived from nonlymphoid organs displayed a modulatory profile, while splenic Vβ9+ T cells produced inflammatory cytokines, indicating that although they express the same TCR Vβ region, these cells are functionally distinct. Information on TCR expression, specificity and function have critical impact on vaccine design. [ABSTRACT FROM AUTHOR]

Ebola virus (EBOV) targets immune cells and tries to inactivate dendritic cells and interferon molecules to continue its replication process. Since EBOV detailed mechanism has not been identified so far, it would be useful to understand the growth and spread of EBOV dynamics based on mathematical methods and simulation approaches. Computational approaches such as Cellular Automata (CA) have the advantage of simplicity over solving complicated differential equations. The spread of Ebola virus in lymph nodes is studied using a simplified Cellular Automata model with only four parameters. In addition to considering healthy and infected cells, this paper also considers T lymphocytes as well as cell movement ability during the simulation in order to investigate different scenarios in the dynamics of an EBOV system. It is shown that the value of the probability of death of T cells affects the number of infected cells significantly in the steady-state. For a special case of parameters set, the system shows oscillating dynamics. The results were in good agreement with an ordinary differential equation-based model which indicated CA method in combination with experimental discoveries could help biologists find out more about the EBOV mechanism and hopefully to control the disease. [ABSTRACT FROM AUTHOR]

HIV-1 establishes a persistent proviral reservoir by integrating into the genome of infected host cells. Current antiretroviral treatments do not target this persistent population of proviruses which include latently infected cells that upon treatment interruption can be reactivated to contribute to HIV-1 rebound. Deep sequencing of persistent HIV proviruses has revealed that greater than 90% of integrated HIV genomes are defective and unable to produce infectious virions. We hypothesized that intragenic elements in the HIV genome support transcription of aberrant HIV-1 RNAs from defective proviruses that lack long terminal repeats (LTRs). Using an intact provirus detection assay, we observed that resting CD4+ T cells and monocyte-derived macrophages (MDMs) are biased towards generating defective HIV-1 proviruses. Multiplex reverse transcription droplet digital PCR identified env and nef transcripts which lacked 5' untranslated regions (UTR) in acutely infected CD4+ T cells and MDMs indicating transcripts are generated that do not utilize the promoter within the LTR. 5'UTR-deficient env transcripts were also identified in a cohort of people living with HIV (PLWH) on ART, suggesting that these aberrant RNAs are produced in vivo. Using 5' rapid amplification of cDNA ends (RACE), we mapped the start site of these transcripts within the Env gene. This region bound several cellular transcription factors and functioned as a transcriptional regulatory element that could support transcription and translation of downstream HIV-1 RNAs. These studies provide mechanistic insights into how defective HIV-1 proviruses are persistently expressed to potentially drive inflammation in PLWH. Author summary: People living with HIV establish a persistent reservoir which includes latently infected cells that fuel viral rebound upon treatment interruption. However, the majority of HIV-1 genomes in these persistently infected cells are defective. Whether these defective HIV genomes are expressed and whether they contribute to HIV associated diseases including accelerated aging, neurodegenerative symptoms, and cardiovascular diseases are still outstanding questions. In this paper, we demonstrate that acute infection of macrophages and resting T cells is biased towards generating defective viruses which are expressed by DNA regulatory elements in the HIV genome. These studies describe an alternative mechanism for chronic expression of HIV genomes. [ABSTRACT FROM AUTHOR]

In this essay, we show that 3 distinct approaches to immunological exhaustion coexist and that they only partially overlap, generating potential misunderstandings. Exploring cases ranging from viral infections to cancer, we propose that it is crucial, for experimental and therapeutic purposes, to clarify these approaches and their interconnections so as to make the concept of exhaustion genuinely operational. Author summary: In this essay, we have written a critical review on immunological exhaustion. We believe that this widely used concept often remains in fact imprecise because there exist 3 different approaches to exhaustion, namely, in terms of dysfunction, cause, and marker, and those are not sufficiently well distinguished and articulated in most scientific papers. We also propose to talk about "exhaustion" for and only for the phenomena in which all 3 approaches are aligned. This can be called the "convergence strategy": T cells would be described as "exhausted" if and only if they are simultaneously dysfunctional, express given markers such as programmed cell death protein 1 (PD-1), and induced by a specific cause such as chronicity. This strategy could be perfectly reasonable, and we see much value in adopting it, because it would force everyone to be more specific and rigorous when talking about exhaustion. Clarifying the characteristics of cells defined as "exhausted," and using a convergent strategy to define T-cell exhaustion could have major experimental and clinical consequences, including for viral infections such as the Coronavirus Disease 2019 (COVID-19). [ABSTRACT FROM AUTHOR]