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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 cell type signatures vs. immune disease signatures. Two-hundred-eighty-seven human and mouse immune cell type signatures were paired against 155 immune disease signatures. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Cell type signatures were positioned according to their common cell lineage, and disease signatures were positioned according to their disease category. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity
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Fig6: Similarity matrix of immune cell type signatures vs. immune disease signatures. Two-hundred-eighty-seven human and mouse immune cell type signatures were paired against 155 immune disease signatures. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Cell type signatures were positioned according to their common cell lineage, and disease signatures were positioned according to their disease category. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity

Mentions: Autoimmune diseases involve immune cell activation and recruitment to the disease tissue. With the availability of both immune cell type signatures and disease signatures, we evaluated their similarity, for the purpose of elucidating the enrichment of cell type signatures in disease tissue. Among all the cell type signatures, some from the myeloid lineage show the most enrichment with different diseases (Fig. 6). In addition, subsets of cell type signatures are more enriched a subpopulation of disease signatures (Additional file 1: Figure S3). For example, some signatures from either T cell or B cell lineages show enrichment in signatures obtained from dermatitis, psoriasis, asthma and arthritis. Some signatures from the myeloid lineage are enriched in IBD and certain populations of lupus. Stromal modules, however, are enriched mainly in IBD signatures. This finding supports previous observation [13].Fig. 6


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 cell type signatures vs. immune disease signatures. Two-hundred-eighty-seven human and mouse immune cell type signatures were paired against 155 immune disease signatures. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Cell type signatures were positioned according to their common cell lineage, and disease signatures were positioned according to their disease category. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Similarity matrix of immune cell type signatures vs. immune disease signatures. Two-hundred-eighty-seven human and mouse immune cell type signatures were paired against 155 immune disease signatures. Similarity was calculated by Fisher’s exact test of overlapping genes for each pair. Cell type signatures were positioned according to their common cell lineage, and disease signatures were positioned according to their disease category. Color represents the –log (P value of Fisher’s exact test), with red color indicating high similarity, and blue color indicating less/no similarity
Mentions: Autoimmune diseases involve immune cell activation and recruitment to the disease tissue. With the availability of both immune cell type signatures and disease signatures, we evaluated their similarity, for the purpose of elucidating the enrichment of cell type signatures in disease tissue. Among all the cell type signatures, some from the myeloid lineage show the most enrichment with different diseases (Fig. 6). In addition, subsets of cell type signatures are more enriched a subpopulation of disease signatures (Additional file 1: Figure S3). For example, some signatures from either T cell or B cell lineages show enrichment in signatures obtained from dermatitis, psoriasis, asthma and arthritis. Some signatures from the myeloid lineage are enriched in IBD and certain populations of lupus. Stromal modules, however, are enriched mainly in IBD signatures. This finding supports previous observation [13].Fig. 6

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