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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

Clustering of immune drug target gene sets vs. immune disease signatures. One-hundred-twenty-six immune drug target gene sets were paired against 155 immune disease signatures. Hierarchical clustering was performed based on the similarity that was calculated by Fisher’s exact test of the overlapping genes for each pair. (I) Heatmap shows the clustering of drug target gene sets (columns) vs. the disease signatures (rows). A, B lists the diseases that are represented by disease signatures showing similarity with drug target gene sets indicated by C1 and C2. Bottom bar chart indicates the number of linked diseases for each drug target shown in the same order as the above heatmap. (II) C1, C2 tables list the details of drug targets and their linked diseases for drug target gene set clusters shown in (I)
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Fig7: Clustering of immune drug target gene sets vs. immune disease signatures. One-hundred-twenty-six immune drug target gene sets were paired against 155 immune disease signatures. Hierarchical clustering was performed based on the similarity that was calculated by Fisher’s exact test of the overlapping genes for each pair. (I) Heatmap shows the clustering of drug target gene sets (columns) vs. the disease signatures (rows). A, B lists the diseases that are represented by disease signatures showing similarity with drug target gene sets indicated by C1 and C2. Bottom bar chart indicates the number of linked diseases for each drug target shown in the same order as the above heatmap. (II) C1, C2 tables list the details of drug targets and their linked diseases for drug target gene set clusters shown in (I)

Mentions: We are able to utilize the immune disease gene signatures to investigate whether the current autoimmune or inflammation related disease drugs or drugs at development are targeting these disease gene signatures. Hence, we built target gene sets with the down-stream genes of those drug targets, and evaluated their overlap with disease gene signatures. The heatmap in Fig. 7I shows the clustering of drug target gene sets vs. disease gene signatures. Both drug target gene set cluster C1 and C2 significantly overlap with disease signature cluster A, which mainly represents diseases of psoriasis, dermatitis, IBD, arthritis, and lupus. This is in agreement with the disease indications for most drugs in both C1 and C2 lists (Fig. 7II). However, drug target gene set, cluster C1, also shows significant overlap with the disease signature cluster B, which are enriched with asthma gene signatures. To ensure the validity of our gene signatures, we plotted the targets, which are either approved drugs or drugs under development, with their linked diseases. Drugs linked with more diseases also had more significant overlap with different disease signatures (Fig. 7I), suggesting that our gene signatures represent the gene structure of the disease. For drug target gene sets that are less significantly overlapped with disease signatures, most of them distributed at the left quarter side of the heatmap, their drugs are most likely linked with fewer diseases, and the majority of them are specifically targeting MS. In the bottom bar showing the number of disease indications associated with each target gene, annexin A1, associated with glucocorticoid’s downstream pathway, has the highest number of linked diseases. This gene may play a general role towards the function of these diseases through this steroid pathway; however, we did not find it useful in identifying specific immunological disease manifestations.Fig. 7


Gene signature-based mapping of immunological systems and diseases.

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

Clustering of immune drug target gene sets vs. immune disease signatures. One-hundred-twenty-six immune drug target gene sets were paired against 155 immune disease signatures. Hierarchical clustering was performed based on the similarity that was calculated by Fisher’s exact test of the overlapping genes for each pair. (I) Heatmap shows the clustering of drug target gene sets (columns) vs. the disease signatures (rows). A, B lists the diseases that are represented by disease signatures showing similarity with drug target gene sets indicated by C1 and C2. Bottom bar chart indicates the number of linked diseases for each drug target shown in the same order as the above heatmap. (II) C1, C2 tables list the details of drug targets and their linked diseases for drug target gene set clusters shown in (I)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Clustering of immune drug target gene sets vs. immune disease signatures. One-hundred-twenty-six immune drug target gene sets were paired against 155 immune disease signatures. Hierarchical clustering was performed based on the similarity that was calculated by Fisher’s exact test of the overlapping genes for each pair. (I) Heatmap shows the clustering of drug target gene sets (columns) vs. the disease signatures (rows). A, B lists the diseases that are represented by disease signatures showing similarity with drug target gene sets indicated by C1 and C2. Bottom bar chart indicates the number of linked diseases for each drug target shown in the same order as the above heatmap. (II) C1, C2 tables list the details of drug targets and their linked diseases for drug target gene set clusters shown in (I)
Mentions: We are able to utilize the immune disease gene signatures to investigate whether the current autoimmune or inflammation related disease drugs or drugs at development are targeting these disease gene signatures. Hence, we built target gene sets with the down-stream genes of those drug targets, and evaluated their overlap with disease gene signatures. The heatmap in Fig. 7I shows the clustering of drug target gene sets vs. disease gene signatures. Both drug target gene set cluster C1 and C2 significantly overlap with disease signature cluster A, which mainly represents diseases of psoriasis, dermatitis, IBD, arthritis, and lupus. This is in agreement with the disease indications for most drugs in both C1 and C2 lists (Fig. 7II). However, drug target gene set, cluster C1, also shows significant overlap with the disease signature cluster B, which are enriched with asthma gene signatures. To ensure the validity of our gene signatures, we plotted the targets, which are either approved drugs or drugs under development, with their linked diseases. Drugs linked with more diseases also had more significant overlap with different disease signatures (Fig. 7I), suggesting that our gene signatures represent the gene structure of the disease. For drug target gene sets that are less significantly overlapped with disease signatures, most of them distributed at the left quarter side of the heatmap, their drugs are most likely linked with fewer diseases, and the majority of them are specifically targeting MS. In the bottom bar showing the number of disease indications associated with each target gene, annexin A1, associated with glucocorticoid’s downstream pathway, has the highest number of linked diseases. This gene may play a general role towards the function of these diseases through this steroid pathway; however, we did not find it useful in identifying specific immunological disease manifestations.Fig. 7

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