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Background <p>The functional impact of genomic variation during active tuberculosis disease and anti-tuberculosis treatment, respectively, remains poorly understood owing to a paucity of in vivo context-specific genome-wide molecular quantitative trait loci studies for these phenotypic traits. Characterising the context-specific functional impact of sequence polymorphisms represents a necessary endeavour to provide new insight into how regulatory variation modulates complex traits, such as susceptibility to <i>Mycobacterium tuberculosis</i> infection.</p> Methods <p>Using 240 paired whole-blood RNA-seq samples from <i>n</i> = 48 active tuberculosis patients who subsequently underwent anti-TB treatment, we called and imputed genome-wide variants from these transcriptomes and mapped <i>cis</i>-expression quantitative trait loci (eQTL), response-eQTL (reQTL), and cell type interaction eQTL (ieQTL) after computationally deconvolving the bulk RNA-seq data using peripheral blood mononuclear single-cell RNA-seq data from <i>n</i> = 5 TB-infected patients as a reference.</p> Results <p>Here, we characterise 1,506,948 high-quality imputed genome-wide variants from the transcriptomics data. We identify a total of 5356 <i>cis-</i>eQTL and 790 reQTL and show that genes associated with reQTL are significantly enriched in pathways that impact the host response to mycobacterial challenge and xenobiotic metabolism. Additionally, we highlight significant changes in the proportions of computationally deconvolved cell types during anti-tuberculosis treatment, notably for natural killer cells and classical monocytes. Leveraging these deconvolved cell type proportions, we characterise a total of 1098 ieQTL, including an active-TB classical monocyte-specific ieQTL for the gene <i>ALOX5</i>.</p> Conclusions <p>Our work sheds light on the immunogenetics of tuberculosis disease and treatment and provides a framework for integrative genomics studies using only RNA-seq data.</p>

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Genetic control of the transcriptional response to active tuberculosis disease and treatment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • John F. O’Grady,
  • Alexander S. Leonard,
  • Houcheng Li,
  • Lingzhao Fang,
  • Hubert Pausch,
  • Isobel C. Gormley,
  • Stephen. V. Gordon,
  • David E. MacHugh

摘要

Background

The functional impact of genomic variation during active tuberculosis disease and anti-tuberculosis treatment, respectively, remains poorly understood owing to a paucity of in vivo context-specific genome-wide molecular quantitative trait loci studies for these phenotypic traits. Characterising the context-specific functional impact of sequence polymorphisms represents a necessary endeavour to provide new insight into how regulatory variation modulates complex traits, such as susceptibility to Mycobacterium tuberculosis infection.

Methods

Using 240 paired whole-blood RNA-seq samples from n = 48 active tuberculosis patients who subsequently underwent anti-TB treatment, we called and imputed genome-wide variants from these transcriptomes and mapped cis-expression quantitative trait loci (eQTL), response-eQTL (reQTL), and cell type interaction eQTL (ieQTL) after computationally deconvolving the bulk RNA-seq data using peripheral blood mononuclear single-cell RNA-seq data from n = 5 TB-infected patients as a reference.

Results

Here, we characterise 1,506,948 high-quality imputed genome-wide variants from the transcriptomics data. We identify a total of 5356 cis-eQTL and 790 reQTL and show that genes associated with reQTL are significantly enriched in pathways that impact the host response to mycobacterial challenge and xenobiotic metabolism. Additionally, we highlight significant changes in the proportions of computationally deconvolved cell types during anti-tuberculosis treatment, notably for natural killer cells and classical monocytes. Leveraging these deconvolved cell type proportions, we characterise a total of 1098 ieQTL, including an active-TB classical monocyte-specific ieQTL for the gene ALOX5.

Conclusions

Our work sheds light on the immunogenetics of tuberculosis disease and treatment and provides a framework for integrative genomics studies using only RNA-seq data.