Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

Clinical predictive value of the CRP-albumin-lymphocyte index for prognosis of critically ill patients with sepsis in intensive care unit: a retrospective single-center observational study

Background

Sepsis is a complex syndrome characterized by physiological, pathological, and biochemical abnormalities caused by infection. Its development is influenced by factors such as inflammation, nutrition, and immune status. Therefore, we combined C-reactive protein (CRP), albumin, and lymphocyte, which could reflect above status, to be the CRP-albumin-lymphocyte (CALLY) index, and investigated its association with clinical prognosis of critically ill patients with sepsis.

Methods

This retrospective observational study enrolled critically ill patients with sepsis who had an initial CRP, albumin, and lymphocyte data on the first day of ICU admission. All data were obtained from the Affiliated Hospital of Jiangsu University. The patients were divided into quartiles (Q1-Q4) based on their CALLY index. The outcomes included 30-/60-day mortality and acute kidney injury (AKI) occurrence. The association between the CALLY index and these clinical outcomes in critically ill patients with sepsis was evaluated using Cox proportional hazards and logistic regression analysis.

Results

A total of 1,123 patients (63.0% male) were included in the study. The 30-day and 60-day mortality rates were found to be 28.1 and 33.4%, respectively, while the incidence of AKI was 45.6%. Kaplan-Meier analysis revealed a significant association between higher CALLY index and lower risk of 30-day and 60-day mortality (log-rank p < 0.001). Multivariate Cox proportional hazards analysis indicated that the CALLY index was independently associated with 30-day mortality [HR (95%CI): 0.965 (0.935-0.997); p = 0.030] and 60-day mortality [HR (95%CI): 0.969 (0.941-0.997); p = 0.032]. Additionally, the multivariate logistic regression model showed that the CALLY index served as an independent risk predictor for AKI occurrence [OR (95%CI): 0.982 (0.962-0.998); p = 0.033].

Conclusion

The findings of this study indicated a significant association between the CALLY index and both 30-day and 60-day mortality, as well as the occurrence of AKI, in critically ill patients with sepsis. These findings suggested that the CALLY index may be a valuable tool in identifying sepsis patients who were at high risk for unfavorable outcomes.

B cell lineage reconstitution underlies CAR-T cell therapeutic efficacy in patients with refractory myasthenia gravis

B-cell maturation antigen (BCMA), expressed in plasmablasts and plasma cells, could serve as a promising therapeutic target for autoimmune diseases. We reported here chimeric antigen receptor (CAR) T cells targeting BCMA in two patients with highly relapsed and refractory myasthenia gravis (one with AChR-IgG, and one with MuSk-IgG). Both patients exhibited favorable safety profiles and persistent clinical improvements over 18 months. Reconstitution of B-cell lineages with sustained reduced pathogenic autoantibodies might underlie the therapeutic efficacy. To identify the possible mechanisms underlying the therapeutic efficacy of CAR-T cells in these patients, longitudinal single-cell RNA and TCR sequencing was conducted on serial blood samples post infusion as well as their matching infusion products. By tracking the temporal evolution of CAR-T phenotypes, we demonstrated that proliferating cytotoxic-like CD8 clones were the main effectors in autoimmunity, whereas compromised cytotoxic and proliferation signature and profound mitochondrial dysfunction in CD8+ Te cells before infusion and subsequently defect CAR-T cells after manufacture might explain their characteristics in these patients. Our findings may guide future studies to improve CAR T-cell immunotherapy in autoimmune diseases.

Upregulation of CD226 on subsets of T cells and NK cells is associated with upregulated adhesion molecules and cytotoxic factors in patients with tuberculosis

Human T cells and natural killer (NK) cells are major effector cells of innate immunity exerting potential immune surveillance against tuberculosis infection. CD226 is an activating receptor playing vital roles in the functions of T cells and NK cells during HIV infection and tumorigenesis. However, CD226 is a less-studied activating receptor during Mycobacterium tuberculosis (Mtb) infection. In this study, we used peripheral blood from tuberculosis patients and healthy donors to evaluate CD226 immunoregulation functions from two independent cohorts using Flow cytometry. Here, we found that a subset of T cells and NK cells that constitutively express CD226 exhibit a distinct phenotype in TB patients. In fact, the proportions of CD226⁺ and CD226^- cell subsets differ between healthy people and tuberculosis patients, and the expression of immune checkpoint molecules (TIGIT, NKG2A) and adhesion molecules (CD2, CD11a) in CD226⁺ and CD226^- subsets of T cells and NK cells exhibits special regulatory roles. Furthermore, CD226⁺ subsets produced more IFN-γ and CD107a than CD226^- subsets in tuberculosis patients. Our results imply that CD226 may be a potential predictor of disease progression and clinical efficacy in tuberculosis by mediating the cytotoxic capacity of T cells and NK cells.

Decrease in cell counts and alteration of phenotype characterize peripheral NK cells of patients with anti-MDA5-positive dermatomyositis

OBJECTIVE

To investigate the levels and phenotypes of peripheral natural killer (NK) cells in anti-MDA5+ dermatomyositis (DM) patients, and their association with clinical features.

METHODS

Peripheral NK cell counts (NKCCs) were retrospectively collected from 497 patients with idiopathic inflammatory myopathies and 60 healthy controls. Multi-color flow cytometry was used to determine the NK cell phenotype in additional 48 DM patients and 26 healthy controls. The association of NKCC and NK cell phenotypes with the clinical features and prognosis were analyzed in anti-MDA5+ DM patients.

RESULTS

NKCC was significantly lower in anti-MDA5+ DM patients than in those with other IIM subtypes and healthy controls. A significant decrease in NKCC was associated with disease activity. Furthermore, NKCC < 27 cells/μL was an independent risk factor for 6-month mortality in anti-MDA5+ DM patients. In addition, identification of the functional phenotype of NK cells revealed significantly increased expression of the inhibitory marker CD39 in CD56^brightCD16^dimNK cells of anti-MDA5+ DM patients. CD39⁺NK cells of anti-MDA5+ DM patients showed increased expression of NKG2A, NKG2D, Ki-67, decreased expression of Tim-3, LAG-3, CD25, CD107a, and reduced TNF-α production.

CONCLUSION

Decreased cell counts and inhibitory phenotype are significant characteristics of peripheral NK cells in anti-MDA5+ DM patients.

Convolutional neural network based on T-SPOT.TB assay promoting the discrimination between active tuberculosis and latent tuberculosis infection

OBJECTIVES

The study aims to investigate the potential of convolutional neural network (CNN) based on spot image of T-SPOT assay for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI).

METHODS

CNN was applied to recognize and classify T-SPOT spot image. Logistic regression was used to establish prediction model based on CNN.

RESULTS

Areas under the receiver operating characteristic curve (AUCs) of early secreted antigenic target 6 (ESAT-6) CNN, culture filtrate protein 10 (CFP-10) CNN, and phytohemagglutinin (PHA) CNN were more than 0.7 in differentiating ATB from LTBI, while the performance of these indicators was significantly better than that of spot number. Furthermore, prediction model based on the combination of CNNs yielded an AUC of 0.898. The model presented a sensitivity of 85.76% and a specificity of 90.23%.

CONCLUSIONS

The current study identified CNN based on T-SPOT spot image with the potential to serve as a tool for TB diagnostics.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

BACKGROUND

The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.

METHODS

T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.

RESULTS

A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.

CONCLUSIONS

Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

Immune responses in COVID-19 and tuberculosis coinfection: A scoping review

Background and aim

Patients with COVID-19 and tuberculosis coinfection are at an increased risk of severe disease and death. We therefore sought to evaluate the current evidence which assessed the immune response in COVID-19 and tuberculosis coinfection

Methods

We searched Pubmed/MEDLINE, EMBASE, Scopus, and Web of Science to identify articles published between 2020 and 2021. We included observational studies evaluating the immune response in patients with tuberculosis and COVID-19 compared to patients with COVID-19 alone.

Results

Four cross-sectional studies (372 participants) were identified. In patients with asymptomatic COVID-19 and latent tuberculosis (LTBI), increased cytokines, chemokines, growth factors and humoral responses were found. In addition, patients with symptomatic COVID-19 and LTBI had higher leukocytes counts and less inflammation. Regarding patients with COVID-19 and active tuberculosis (aTB), they exhibited decreased total lymphocyte counts, CD4 T cells specific against SARS-CoV-2 and responsiveness to SARS-CoV-2 antigens compared to patients with only COVID-19.

Conclusion

Although the evidence is limited, an apparent positive immunomodulation is observed in patients with COVID-19 and LTBI. On the other hand, patients with COVID-19 and aTB present a dysregulated immune response. Longitudinal studies are needed to confirm these findings and expand knowledge.

Differential activation of innate and adaptive lymphocytes during latent or active infection with Mycobacterium tuberculosis

Most individuals infected with Mycobacterium tuberculosis (Mtb) have latent tuberculosis (TB), which can be diagnosed with tests (like the QuantiFERON test, QFT) that detect the production of IFN-γ by memory T cells in response to the Mtb-specific antigens ESAT-6, CFP-10 and TB7.7. However, the immunological mechanisms that determine if an individual will develop latent or active TB remain incompletely understood. Here we compared the response of innate and adaptive peripheral blood lymphocytes from healthy individuals without Mtb infection (QFT-negative) and from individuals with latent (QFT-positive) or active TB infection, in order to determine the characteristics of these cells that correlate with each condition. In active TB patients, the levels of IFN-γ that were produced in response to Mtb-specific antigens had high positive correlations with IL-1β, TNF-α, MCP-1, IL-6, IL-12p70 and IL-23, while the pro-inflammatory cytokines had high positive correlations between themselves and with IL-12p70 and IL-23. These correlations were not observed in QFT-negative or QFT-positive healthy volunteers. Activation with Mtb soluble extract (a mixture of Mtb antigens and pathogen-associated molecular patterns [PAMPs]) increased the percentage of IFN-γ/IL-17-producing NK cells and of IL-17-producing ILC3 in the peripheral blood of active TB patients, but not of QFT-negative or QFT-positive healthy volunteers. Thus, active TB patients have both adaptive and innate lymphocyte subsets that produce characteristic cytokine profiles in response to Mtb-specific antigens or PAMPs. These profiles are not observed in uninfected individuals or in individuals with latent TB, suggesting that they are a response to active TB infection. This article is protected by copyright. All rights reserved.

Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

BACKGROUND

Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI.

METHODS

Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning.

RESULTS

A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively.

CONCLUSIONS

We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.

Revisiting tuberculosis screening: An insight to complementary diagnosis and prospective molecular approaches for the recognition of the dormant TB infection in human and cattle hosts

Tuberculosis (TB) is defined as a chronic infection in both human and cattle hosts and many subclinical cases remain undetected. After the pathogen is inhaled by a host, phagocyted bacilli can persist inside macrophages surviving intracellularly. Hosts develop granulomatous lesions in the lungs or lymph nodes, limiting infection. However, bacilli become persister cells. Immunological diagnosis of TB is performed basically by routine tuberculin skin test (TST), and in some cases, by ancillary interferon-gamma release assay (IGRA). The concept of human latent TB infection (LTBI) by M. tuberculosis is recognized in cohorts without symptoms by routine clinical diagnostic tests, and nowadays IGRA tests are used to confirm LTBI with either active or latent specific antigens of M. tuberculosis. On the other hand, dormant infection in cattle by M. bovis has not been described by TST or IGRA testing as complications occur by cross-reactive immune responses to homolog antigens of environmental mycobacteria or a false-negative test by anergic states of a wained bovine immunity, evidencing the need for deciphering more specific biomarkers by new-generation platforms of analysis for detection of M. bovis dormant infection. The study and description of bovine latent TB infection (boLTBI) would permit the recognition of hidden animal infection with an increase in the sensitivity of routine tests for an accurate estimation of infected dairy cattle. Evidence of immunological and experimental analysis of LTBI should be taken into account to improve the study and the description of the still neglected boLTBI.

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

Background

Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue.

Methods

Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4⁺ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation.

Results

A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α⁺ or IL-2⁺ cells had superiority over that on IFN-γ⁺ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α⁺IL-2⁺ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α⁺IL-2⁺ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity.

Conclusions

We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.