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Reanalysis and integration of public microarray datasets reveals novel host genes modulated in leprosy

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Abstract

Due to multiple hypothesis testing with often limited sample size, microarrays and other—omics technologies can sometimes produce irreproducible findings. Complementary to better experimental design, reanalysis and integration of gene expression datasets may help overcome reproducibility issues by identifying consistent differentially expressed genes from independent studies. In this work, after a systematic search, nine microarray datasets evaluating host gene expression in leprosy were reanalyzed and the information was integrated to strengthen evidence of differential expression for several genes. Our results are relevant in prioritizing genes and pathways for further investigation, whether in functional studies or in biomarker discovery. Reanalysis of individual datasets revealed several differentially expressed genes (DEGs) in accordance with original reports. Then, five integration methods (P value and effect size based) were tested. In the end, random-effects model and ratio association were selected as the main methods to pinpoint DEGs. Overall, classic pathways were found corroborating previous findings and validating this approach. Also, we identified some novel DEG involved especially with skin development processes (AQP3, AKR1C3, CYP27B1, LTB, VDR) and keratinocyte biology (CSTA, DSG1, KRT14, KRT5, PKP1, IVL), both still poorly understood in leprosy context. In addition, here we provide aggregated evidence towards some gene candidates that should be prioritized in further leprosy research, as they are likely important in immunopathogenesis. Altogether, these data are useful in better understanding host responses to the disease and, at the same time, provide a list of potential host biomarkers that could be useful in complementing leprosy diagnosis based on transcriptional levels.

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Data availability

All datasets analyzed during the current study are available under NCBI’s Gene Expression Omnibus repository accessions: GSE40950 (Guerreiro et al. 2013), GSE35423 (de Toledo-Pinto et al. 2016), GSE24280 [unknown], GSE443 (Bleharski et al. 2003), GSE17763 (Montoya et al. 2009), GSE16844 (Lee et al. 2010), GSE74481 (Belone et al. 2015), GSE95748 (Masaki et al. 2013), and GSE100853 (Manry et al. 2017). In addition, all R computer source code and data used in the analyzes are readily available at GitHub (https://github.com/thyagoleal/leprosy_reanalysis_paper) and Zenodo (https://doi.org/10.5281/zenodo.3840319).

Abbreviations

sdef:

Ratio association method

REM:

Random-effects model

rOP :

r-th ordered P-value

maxP :

Maximum P-value

SR:

Sum of ranks

MB:

Multibacillary leprosy

PB:

Paucibacillary leprosy

DEG:

Differentially expressed gene

FDR:

False discovery rate

BH:

Benjamini–Hochberg

LL:

Lepromatous leprosy

ENL:

Erythema nodosum leprosum

BT:

Borderline-tuberculoid

FC:

Fold change

GEO:

Gene Expression Omnibus

NCBI:

National Center for Biotechnology Information

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Acknowledgements

TLC was supported by a scholarship from the Oswaldo Cruz Institute (IOC-FIOCRUZ) from July (2016) to June (2018). We also thank the Heiser Foundation and Novartis Foundation for their financial support. The funding agencies had no involvement in the study elaboration, data analysis and interpretation or publishing process.

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  1. Laboratório de Hanseníase, Instituto Oswaldo Cruz, FIOCRUZ, Rio De Janeiro, 21040-360, Brazil

    Thyago Leal-Calvo & Milton Ozório Moraes

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  1. Thyago Leal-Calvo
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  2. Milton Ozório Moraes
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TLC analyzed the study, interpreted data and drafted the manuscript. MOM conceptualized the study, interpreted results and reviewed the manuscript.

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Correspondence to Milton Ozório Moraes.

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Electronic supplementary material

Below is the link to the electronic supplementary material.

438_2020_1705_MOESM1_ESM.zip

Supplementary Material 1—Supplementary tables S1–S4, GEO search results and detailed methods on individual study reanalysis. Full table of results for the integration/meta-analysis with FDR ≤ 0.1. This file contains four .XLS spreadsheets with results for the LL vs. Control and Stimulated vs. Control (in vitro) categories not presented within main text. One .PDF containing the 18 results from the GEOquery. One .PDF containing detailed methods used in reanalyzing each dataset individually. (ZIP 2235 kb)

438_2020_1705_MOESM2_ESM.zip

Supplementary Material 2—Supplementary Figures S1–S2, REM forest plots, multidimensional scaling (MDS) plot with top 500 most variable genes from individual datasets. Enrichment analysis for the LL vs. Control and Stimulated vs. Control (in vitro) categories and forest plots for DEGs from LL vs. BT and LL vs. ENL random effects model (REM) estimates. MDS plots from individual datasets reanalysis. (ZIP 3199 kb)

438_2020_1705_MOESM3_ESM.zip

Supplementary material 3—Supplementary Tables S5–S8 Four .XLS spreadsheets containing full enrichment results for all categories analyzed its categorical label. (ZIP 195 kb)

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Leal-Calvo, T., Moraes, M.O. Reanalysis and integration of public microarray datasets reveals novel host genes modulated in leprosy. Mol Genet Genomics 295, 1355–1368 (2020). https://doi.org/10.1007/s00438-020-01705-6

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