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Gene signature-based mapping of immunological systems and diseases.

Liu H, Liu J, Toups M, Soos T, Arendt C - BMC Bioinformatics (2016)

Bottom Line: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states.This modular 'biomap' reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues.The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

View Article: PubMed Central - PubMed

Affiliation: Bio-Innovation, Sanofi Global Biotherapeutics, 38 Sidney Street, Cambridge, MA, 02139, USA. hong.liu@sanofi.com.

ABSTRACT

Background: The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease.

Result: We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches.

Conclusion: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular 'biomap' reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

No MeSH data available.


Related in: MedlinePlus

Over-represented genes in immune disease modules. Twenty-two upregulated genes, found to be common to more than three signature disease gene lists, are illustrated. S100A9 is common to 5 diseases while CCL2 is common among 4 different diseases
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Fig3: Over-represented genes in immune disease modules. Twenty-two upregulated genes, found to be common to more than three signature disease gene lists, are illustrated. S100A9 is common to 5 diseases while CCL2 is common among 4 different diseases

Mentions: Figure 3 shows genes common to three or more of the signature disease gene lists. Of particular note, S100A9 is associated with most diseases, including arthritis, lupus, IBD, psoriasis, and dermatitis. This implies that it is up-regulated in a high percentage of samples associated with those diseases. The second most highly represented gene is CCL2, which links with four diseases: lupus, IBD, COPD, and dermatitis. None of the genes in Fig. 3 are common to asthma, sclerosis, or T1D, which is consistent with the smaller overall numbers of associated signature disease genes for these diseases.Fig. 3


Gene signature-based mapping of immunological systems and diseases.

Liu H, Liu J, Toups M, Soos T, Arendt C - BMC Bioinformatics (2016)

Over-represented genes in immune disease modules. Twenty-two upregulated genes, found to be common to more than three signature disease gene lists, are illustrated. S100A9 is common to 5 diseases while CCL2 is common among 4 different diseases
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4836068&req=5

Fig3: Over-represented genes in immune disease modules. Twenty-two upregulated genes, found to be common to more than three signature disease gene lists, are illustrated. S100A9 is common to 5 diseases while CCL2 is common among 4 different diseases
Mentions: Figure 3 shows genes common to three or more of the signature disease gene lists. Of particular note, S100A9 is associated with most diseases, including arthritis, lupus, IBD, psoriasis, and dermatitis. This implies that it is up-regulated in a high percentage of samples associated with those diseases. The second most highly represented gene is CCL2, which links with four diseases: lupus, IBD, COPD, and dermatitis. None of the genes in Fig. 3 are common to asthma, sclerosis, or T1D, which is consistent with the smaller overall numbers of associated signature disease genes for these diseases.Fig. 3

Bottom Line: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states.This modular 'biomap' reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues.The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

View Article: PubMed Central - PubMed

Affiliation: Bio-Innovation, Sanofi Global Biotherapeutics, 38 Sidney Street, Cambridge, MA, 02139, USA. hong.liu@sanofi.com.

ABSTRACT

Background: The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease.

Result: We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches.

Conclusion: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular 'biomap' reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

No MeSH data available.


Related in: MedlinePlus