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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

Similarity matrix of immune disease gene signatures. One hundred fifty-five Immune disease gene signatures were paired against each other. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Gene signatures from the same disease category were positioned together. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity. Black boxes group the gene signatures that represent the same disease category
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Fig2: Similarity matrix of immune disease gene signatures. One hundred fifty-five Immune disease gene signatures were paired against each other. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Gene signatures from the same disease category were positioned together. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity. Black boxes group the gene signatures that represent the same disease category

Mentions: The similarity matrix derived from these disease gene signatures illustrates that gene signatures from the same disease tend to cluster with one another (Fig. 2). In addition, gene signatures from the same tissue origin, for instance dermatitis and psoriasis, showed higher similarity to each other than to those from other tissues. Most lupus gene signatures were from studies based on blood samples. They show high similarity among themselves, cluster closely with those from synovial fluid (arthritis), and also show cross-similarity to some of the gene signatures generated from colon mucosal biopsies (IBD). In contrast, gene signatures for sclerosis and T1D are distinct from those of other diseases. Those derived from different tissue samples are very different from each other although they represent the same disease (Additional file 1: Figure S2).Fig. 2


Gene signature-based mapping of immunological systems and diseases.

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

Similarity matrix of immune disease gene signatures. One hundred fifty-five Immune disease gene signatures were paired against each other. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Gene signatures from the same disease category were positioned together. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity. Black boxes group the gene signatures that represent the same disease category
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Similarity matrix of immune disease gene signatures. One hundred fifty-five Immune disease gene signatures were paired against each other. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Gene signatures from the same disease category were positioned together. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity. Black boxes group the gene signatures that represent the same disease category
Mentions: The similarity matrix derived from these disease gene signatures illustrates that gene signatures from the same disease tend to cluster with one another (Fig. 2). In addition, gene signatures from the same tissue origin, for instance dermatitis and psoriasis, showed higher similarity to each other than to those from other tissues. Most lupus gene signatures were from studies based on blood samples. They show high similarity among themselves, cluster closely with those from synovial fluid (arthritis), and also show cross-similarity to some of the gene signatures generated from colon mucosal biopsies (IBD). In contrast, gene signatures for sclerosis and T1D are distinct from those of other diseases. Those derived from different tissue samples are very different from each other although they represent the same disease (Additional file 1: Figure S2).Fig. 2

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