Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler

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Abstract

Background

Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition.

Methods

We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing.

Results

The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662–0.989. Three gene sets were not significantly predictive.

Conclusion

Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05598-z.

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Most cited references 57

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

David DeLong, Elizabeth Delong, Daniel L Clarke-Pearson (1988)

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Adjusting batch effects in microarray expression data using empirical Bayes methods.

W Johnson, Cheng Li, Ariel Rabinovic (2007)

Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.

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Author and article information

Contributors

W. Evan Johnson:

ORCID: http://orcid.org/0000-0002-6247-6595

wej@bu.edu

Journal

Journal ID (nlm-ta): BMC Infect Dis

Journal ID (iso-abbrev): BMC Infect Dis

Title: BMC Infectious Diseases

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2334

Publication date (Electronic): 22 January 2021

Publication date PMC-release: 22 January 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 106

Affiliations

[1 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Division of Computational Biomedicine, , Boston University School of Medicine, ; Boston, MA USA

[2 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Bioinformatics Program, , Boston University, ; Boston, MA USA

[3 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Division of Computational Biomedicine and Bioinformatics Program, Boston University, ; Boston, MA USA

[4 ]GRID grid.239424.a, ISNI 0000 0001 2183 6745, Boston Medical Center, ; Boston, MA USA

[5 ]Government Hospital for Chest Diseases, Puducherry, India

[6 ]GRID grid.414953.e, ISNI 0000000417678301, Jawaharlal Institute of Postgraduate Medical Education and Research, ; Puducherry, India

[7 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Epidemiology, , Boston University School of Public Health, ; Boston, MA USA

[8 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Medicine, Center for Emerging Pathogens, , Rutgers New Jersey Medical School, ; Newark, NJ USA

[9 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Section of Infectious Diseases, , Boston University School of Medicine, ; Boston, MA USA

Author information

W. Evan Johnson http://orcid.org/0000-0002-6247-6595

Article

Publisher ID: 5598

DOI: 10.1186/s12879-020-05598-z

PMC ID: 7821401

PubMed ID: 33482742

SO-VID: db6ecef0-42cd-4408-916b-cf3b556d2746

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 June 2020

Date accepted : 9 November 2020

Funding

Funded by: US Civilian Research and Development Foundation

Award ID: 62909, 16963

Award Recipient : W. Evan Johnson

Funded by: NIH

Award ID: R01GM127430-02

Award Recipient : W. Evan Johnson

Funded by: NSF

Award ID: 1559829

Award Recipient : W. Evan Johnson

Custom metadata

ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: tuberculosis,rna-sequencing,gene sets,signatures,biomarkers,latent tuberculosis infection,malnutrition

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ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: tuberculosis, rna-sequencing, gene sets, signatures, biomarkers, latent tuberculosis infection, malnutrition

Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler

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Abstract

Background

Methods

Results

Conclusion

Supplementary Information

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Point-Of-Care Testing for Tuberculosis

Most cited references 57

GSVA: gene set variation analysis for microarray and RNA-Seq data

Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

Adjusting batch effects in microarray expression data using empirical Bayes methods.

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