Dataset Information

Interferon-signaling pathways are upregulated in people with HIV with abnormal pulmonary diffusing capacity (DL CO ).

ABSTRACT:

Objective

People with HIV (PWH) are at greater risk of developing lung diseases even when they are antiretroviral therapy (ART)-adherent and virally suppressed. The most common pulmonary function abnormality in PWH is that of impaired diffusing capacity of the lungs for carbon monoxide (DL CO ), which is an independent risk factor for increased mortality in PWH. Earlier work has identified several plasma biomarkers of inflammation and immune activation to be associated with decreased DL CO . However, the underpinning molecular mechanisms of HIV-associated impaired DL CO are largely unknown.

Design

Cross-sectional pilot study with PWH with normal DL CO (values greater than or equal to the lower limit of normal, DL CO ≥ LLN, N = 9) or abnormal DL CO (DL CO < LLN, N = 9).

Methods

We compared the gene expression levels of over 900 inflammation and immune exhaustion genes in PBMCs from PWH with normal vs. abnormal DL CO using the NanoString technology.

Results

We found that 26 genes were differentially expressed in the impaired DL CO group. These genes belong to 4 categories: 1. Nine genes in inflammation and immune activation pathways, 2. seven upregulated genes that are direct targets of the interferon signaling pathway, 3. seven B-cell specific genes that are downregulated, and 4. three miscellaneous genes. These results were corroborated using the bioinformatics tools DAVID (Database for Annotation, Visualization and Integrated Discovery) and GSEA (Gene Sets Enrichment Analysis).

Conclusion

The data provides preliminary evidence for the involvement of sustained interferon signaling as a molecular mechanism for impaired DL CO in PWH.

SUBMITTER: Zhang M

PROVIDER: S-EPMC11239097 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Interferon-signaling pathways are upregulated in people with HIV with abnormal pulmonary diffusing capacity (DL CO ).

Zhang Michelle M Dai Guorui G Smith Dana L DL Zacco Emanuela E Shimoda Michiko M Kumar Nitasha N Girling Valerie V Gardner Kendall K Hunt Peter W PW Huang Laurence L Lin Jue J

AIDS (London, England) 20240606 10

<h4>Objective</h4>People with HIV (PWH) are at greater risk of developing lung diseases even when they are antiretroviral therapy (ART)-adherent and virally suppressed. The most common pulmonary function abnormality in PWH is that of impaired diffusing capacity of the lungs for carbon monoxide (DL CO ), which is an independent risk factor for increased mortality in PWH. Earlier work has identified several plasma biomarkers of inflammation and immune activation to be associated with decreased DL ...[more]

PMID: 38819840

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Project description:IntroductionThe COVID-19 patients in the convalescent stage noticeably have pulmonary diffusing capacity impairment (PDCI). The pulmonary diffusing capacity is a frequently-used indicator of the COVID-19 survivors' prognosis of pulmonary function, but the current studies focusing on prediction of the pulmonary diffusing capacity of these people are limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting PDCI in the COVID-19 patients using routinely available clinical data, thus assisting the clinical diagnosis.MethodsCollected from a follow-up study from August to September 2021 of 221 hospitalized survivors of COVID-19 18 months after discharge from Wuhan, including the demographic characteristics and clinical examination, the data in this study were randomly separated into a training (80%) data set and a validation (20%) data set. Six popular machine learning models were developed to predict the pulmonary diffusing capacity of patients infected with COVID-19 in the recovery stage. The performance indicators of the model included area under the curve (AUC), Accuracy, Recall, Precision, Positive Predictive Value(PPV), Negative Predictive Value (NPV) and F1. The model with the optimum performance was defined as the optimal model, which was further employed in the interpretability analysis. The MAHAKIL method was utilized to balance the data and optimize the balance of sample distribution, while the RFECV method for feature selection was utilized to select combined features more favorable to machine learning.ResultsA total of 221 COVID-19 survivors were recruited in this study after discharge from hospitals in Wuhan. Of these participants, 117 (52.94%) were female, with a median age of 58.2 years (standard deviation (SD) = 12). After feature selection, 31 of the 37 clinical factors were finally selected for use in constructing the model. Among the six tested ML models, the best performance was accomplished in the XGBoost model, with an AUC of 0.755 and an accuracy of 78.01% after experimental verification. The SHAPELY Additive explanations (SHAP) summary analysis exhibited that hemoglobin (Hb), maximal voluntary ventilation (MVV), severity of illness, platelet (PLT), Uric Acid (UA) and blood urea nitrogen (BUN) were the top six most important factors affecting the XGBoost model decision-making.ConclusionThe XGBoost model reported here showed a good prognostic prediction ability for PDCI of COVID-19 survivors during the recovery period. Among the interpretation methods based on the importance of SHAP values, Hb and MVV contributed the most to the prediction of PDCI outcomes of COVID-19 survivors in the recovery period.

Dataset Information

Interferon-signaling pathways are upregulated in people with HIV with abnormal pulmonary diffusing capacity (DL CO ).

Objective

Design

Methods

Results

Conclusion

Publications

Interferon-signaling pathways are upregulated in people with HIV with abnormal pulmonary diffusing capacity (DL CO ).

Similar Datasets

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