Evolutionary Genetics of Mycobacterium tuberculosis and HIV-1: "The Tortoise and the Hare"

Research progress of single-cell sequencing in tuberculosis

Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.

The contemporary immunoassays for HIV diagnosis: a concise overview

Recent advances in human immunodeficiency virus (HIV) diagnostics have improved the management of disease progression significantly, which have also boosted the efficacy of antiviral therapies. The detection of HIV at the earliest is very important. A highly recognized and effective virological biomarker for acute HIV infections is p24 antigen. This brief overview is based on advances of HIV diagnosis while focusing on the latest HIV testing technologies including HIV-specific antigens detecting assays of both anti-HIV antibodies and p24 antigen. In addition to other emerging molecular diagnostics for acute HIV infection, the utilization of p24 antigen has been summarized. Moreover, it has been explained how these immunoassays have reduced the window period for detection of HIV in the acute stage of infection.

The impact of single-cell genomics on the field of mycobacterial infection

Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.

Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches

Background: T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation via T-cell regulation.

Methods: We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability.

Results: We identified CD4⁺ T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases.

Conclusion: The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.

Coloración de Ziehl-Neelsen en el laboratorio de patología: rendimiento y contribución al diagnóstico de micobacterias en el lavado broncoalveolar

Introducción.

La coloración de Ziehl-Neelsen, con más de 100 años de uso, continúa vigente mundialmente.

Objetivo.

Comparar el rendimiento de las pruebas diagnósticas utilizadas para la determinación de micobacterias en el laboratorio clínico de patología en muestras de lavado broncoalveolar.

Materiales y métodos.

Se revisaron retrospectivamente 737 muestras de lavado broncoalveolar procesadas en el 2019 y el 2020 en el Hospital San Vicente Fundación (Medellín, Colombia) y se compararon las características de tres pruebas diagnósticas realizadas en paralelo: la reacción en cadena de la polimerasa (PCR) para micobacterias con detección de resistencia, el cultivo, y la coloración de Ziehl-Neelsen.

Resultados.

Se catalogaron como enfermos a 93 de los 737 pacientes a partir de los resultados positivos en alguna de las tres pruebas. El cultivo tuvo una sensibilidad de 0,80, la PCR una de 0,76 y la coloración de Ziehl-Neelsen una de 0,51. Sin embargo, solo 5 de 75 (6,5 %) cultivos fueron positivos a las cuatro semanas y el resto lo fue a las ocho semanas. La PCR combinada con la coloración de Ziehl-Neelsen mejoró la sensibilidad de la PCR por sí sola, de 0,76 a 0,88, diferencia que fue estadísticamente signifcativa (p=0,022)

Conclusión.

En las muestras de lavado broncoalveolar, el cultivo sigue siendo la prueba con mejor sensibilidad. El uso conjunto de la prueba de PCR y la coloración de ZiehlNeelsen mejora signifcativamente la sensibilidad de la primera, lo que compensa la demora relativa en la entrega de los resultados debida al tiempo requerido para la tinción de Ziehl-Neelsen.

A three-methylation-driven genes based deep learning model for tuberculosis diagnosis in people with and without human immunodeficiency virus co-infection

Improved diagnostic tests for tuberculosis (TB) among people living with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes of host blood could be used to diagnosis patients co-infected with HIV/TB. In this study, we identified three methylation-driven genes (MDGs) between patients with HIV mono-infection and with HIV/TB co-infection using R package MethylMix. Then, we developed a deep learning model using three MDGs screened which distinguished HIV/TB co-infection from HIV mono-infection with a sensitivity of 95.2% and a specificity of 88.3%. On the two independent datasets, the sensitivity was 80% to 92.8%, respectively; the specificity was 72.7% to 87.5%, respectively. Besides, our deep learning model also accurately classified TB from healthy controls (75.0-100% sensitivity, 91.3-98.1% specificity) and other respiratory disorders (ORDs) (72.7-75.0% sensitivity, 70.9-72.7% specificity). This study will contribute to improve molecular diagnosis for HIV/TB co-infection. This article is protected by copyright. All rights reserved.

Evolutionary dynamics of HIV-1 subtype C in Brazil

The extensive genetic diversity of HIV-1 is a major challenge for the prevention and treatment of HIV-1 infections. Subtype C accounts for most of the HIV-1 infections in the world but has been mainly localized in Southern Africa, Ethiopia and India. For elusive reasons, South Brazil harbors the largest HIV-1 subtype C epidemic in the American continent that is elsewhere dominated by subtype B. To investigate this topic, we collected clinical data and viral sequences from 2611 treatment-naïve patients diagnosed with HIV-1 in Brazil. Molecular epidemiology analysis supported 35 well-delimited transmission clusters of subtype C highlighting transmission within South Brazil but also from the South to all other Brazilian regions and internationally. Individuals infected with subtype C had lower probability to be deficient in CD4⁺ T cells when compared to subtype B. The HIV-1 epidemics in the South was characterized by high female-to-male infection ratios and women-to-child transmission. Our results suggest that HIV-1 subtype C probably takes advantage of longer asymptomatic periods to maximize transmission and is unlikely to outcompete subtype B in settings where the infection of women is relatively less relevant. This study contributes to elucidate factors possibly underlying the geographical distribution and expansion patterns of the most spread HIV-1 subtypes.