TY - JOUR
T1 - SysInflam HuDB, A web resource for mining human blood cells transcriptomic data associated with systemic inflammatory responses to sepsis
AU - Toufiq, Mohammed
AU - Huang, Susie Shih Yin
AU - Boughorbel, Sabri
AU - Alfaki, Mohamed
AU - Rinchai, Darawan
AU - Saraiva, Luis R.
AU - Chaussabel, Damien
AU - Garand, Mathieu
N1 - Funding Information:
This work was supported by the Qatar Foundation and the Qatar National Research Fund Grant NPRP10-0205-170348 awarded to D.C. We thank all the investigators who made their datasets publicly available by depositing them into the NCBI GEO repository. We thank Dr. Alison Russell for valuable opinions and guidance regarding clinical definitions and translational inquiries. We thank Sukanya Dhansingh (Senior Software Engineer, Mindtree Ltd) for valuable guidance and comments regarding software engineering, and Mohammedhusen Khatib (Database Engineer, Sidra Medicine) for valuable comments regarding database architecture. We also thank Dr. Patrick Tang (Division Chief of Pathology, Sidra Medicine) for critical reading of the manuscript.
Funding Information:
This work was supported by the Qatar Foundation and the Qatar National Research Fund Grant NPRP10-0205-170348 awarded to D.C.
Publisher Copyright:
Copyright © 2021 by The American Association of Immunologists, Inc. All rights reserved.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Sepsis develops after a dysregulated host inflammatory response to a systemic infection. Identification of sepsis biomarkers has been challenging because of the multifactorial causes of disease susceptibility and progression. Public transcriptomic data are a valuable resource for mechanistic discoveries and cross-studies concordance of heterogeneous diseases. Nonetheless, the approach requires structured methodologies and effective visualization tools for meaningful data interpretation. Currently, no such database exists for sepsis or systemic inflammatory diseases in human. Hence we curated SysInflam HuDB (http://sepsis.gxbsidra.org/dm3/geneBrowser/list), a unique collection of human blood transcriptomic datasets associated with systemic inflammatory responses to sepsis. The transcriptome collection and the associated clinical metadata are integrated onto a user-friendly and Web-based interface that allows the simultaneous exploration, visualization, and interpretation of multiple datasets stemming from different study designs. To date, the collection encompasses 62 datasets and 5719 individual profiles. Concordance of gene expression changes with the associated literature was assessed, and additional analyses are presented to showcase database utility. Combined with custom data visualization at the group and individual levels, SysInflam HuDB facilitates the identification of specific human blood gene signatures in response to infection (e.g., patients with sepsis versus healthy control subjects) and the delineation of major genetic drivers associated with inflammation onset and progression under various conditions.
AB - Sepsis develops after a dysregulated host inflammatory response to a systemic infection. Identification of sepsis biomarkers has been challenging because of the multifactorial causes of disease susceptibility and progression. Public transcriptomic data are a valuable resource for mechanistic discoveries and cross-studies concordance of heterogeneous diseases. Nonetheless, the approach requires structured methodologies and effective visualization tools for meaningful data interpretation. Currently, no such database exists for sepsis or systemic inflammatory diseases in human. Hence we curated SysInflam HuDB (http://sepsis.gxbsidra.org/dm3/geneBrowser/list), a unique collection of human blood transcriptomic datasets associated with systemic inflammatory responses to sepsis. The transcriptome collection and the associated clinical metadata are integrated onto a user-friendly and Web-based interface that allows the simultaneous exploration, visualization, and interpretation of multiple datasets stemming from different study designs. To date, the collection encompasses 62 datasets and 5719 individual profiles. Concordance of gene expression changes with the associated literature was assessed, and additional analyses are presented to showcase database utility. Combined with custom data visualization at the group and individual levels, SysInflam HuDB facilitates the identification of specific human blood gene signatures in response to infection (e.g., patients with sepsis versus healthy control subjects) and the delineation of major genetic drivers associated with inflammation onset and progression under various conditions.
UR - http://www.scopus.com/inward/record.url?scp=85119063000&partnerID=8YFLogxK
U2 - 10.4049/jimmunol.2100697
DO - 10.4049/jimmunol.2100697
M3 - Article
C2 - 34663591
AN - SCOPUS:85119063000
SN - 0022-1767
VL - 207
SP - 2195
EP - 2202
JO - Journal of Immunology
JF - Journal of Immunology
IS - 9
ER -