Limits...
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

Topological localization of pair of disease genes. Signature disease genes representing two different disease categories and with direct links on the interactome were plotted. (I) Topological locations of dermatitis and psoriasis genes. Orange color: genes unique to dermatitis; green color: genes unique to psoriasis; yellow color: genes shared by dermatitis and psoriasis. (II) Topological locations of COPD and asthma genes. Blue color: genes unique to COPD; rose color: genes unique to asthma
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Topological localization of pair of disease genes. Signature disease genes representing two different disease categories and with direct links on the interactome were plotted. (I) Topological locations of dermatitis and psoriasis genes. Orange color: genes unique to dermatitis; green color: genes unique to psoriasis; yellow color: genes shared by dermatitis and psoriasis. (II) Topological locations of COPD and asthma genes. Blue color: genes unique to COPD; rose color: genes unique to asthma

Mentions: To further evaluate the network-based relationships of signature disease genes, we mapped them to an interactome. To reduce noise and avoid over-linkage, only genes with direct links were retained. In Fig. 4, disease genes from dermatitis and psoriasis were revealed to share common genes, as well as linked genes, while COPD and asthma do not share common genes. Furthermore, genes unique to COPD or asthma contain fewer connections, and are distinct from each other. To quantify and assess the network-based separation of disease genes from different disease categories, we performed pair-wise analysis to calculate the network-based separation score [11] for each pair of disease genes. In Table 3, negative scores indicate that disease genes share overlapping ‘neighborhoods’. These results agree with what we observed in Fig. 2. There are more molecular commonalities between dermatitis, psoriasis, lupus, IBD, and possibly arthritis, than that of other diseases studied. We mapped signature disease genes from all disease categories to a single interactome and represented the number of interactions by gene label size, and the number of diseases it belongs to by node size (Fig. 5). We observed that, in Fig. 5I, many genes shared by multiple diseases (shown in yellow) contain more interactions with other genes, such as STAT1, EGR1, TLR2, CCL2, etc. However, it’s worth noting that genes unique to a disease can also hold a lot of interactions with other signature disease genes, such as STAT3, IL1B, CEBPB, IL10, MMP9, TLR4, EGF, etc. In Fig. 5II, we limited the signature disease genes to three indications: psoriasis, dermatitis, and COPD, which clearly shows their interactions across indications. For example, IL1B, a gene target of multiple drugs targeting different autoimmune diseases, is a signature disease gene for COPD. However, it does not only interact with multiple signature disease genes of COPD, but also directly interacts with many signature disease genes of psoriasis and dermatitis.Fig. 4


Gene signature-based mapping of immunological systems and diseases.

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

Topological localization of pair of disease genes. Signature disease genes representing two different disease categories and with direct links on the interactome were plotted. (I) Topological locations of dermatitis and psoriasis genes. Orange color: genes unique to dermatitis; green color: genes unique to psoriasis; yellow color: genes shared by dermatitis and psoriasis. (II) Topological locations of COPD and asthma genes. Blue color: genes unique to COPD; rose color: genes unique to asthma
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Topological localization of pair of disease genes. Signature disease genes representing two different disease categories and with direct links on the interactome were plotted. (I) Topological locations of dermatitis and psoriasis genes. Orange color: genes unique to dermatitis; green color: genes unique to psoriasis; yellow color: genes shared by dermatitis and psoriasis. (II) Topological locations of COPD and asthma genes. Blue color: genes unique to COPD; rose color: genes unique to asthma
Mentions: To further evaluate the network-based relationships of signature disease genes, we mapped them to an interactome. To reduce noise and avoid over-linkage, only genes with direct links were retained. In Fig. 4, disease genes from dermatitis and psoriasis were revealed to share common genes, as well as linked genes, while COPD and asthma do not share common genes. Furthermore, genes unique to COPD or asthma contain fewer connections, and are distinct from each other. To quantify and assess the network-based separation of disease genes from different disease categories, we performed pair-wise analysis to calculate the network-based separation score [11] for each pair of disease genes. In Table 3, negative scores indicate that disease genes share overlapping ‘neighborhoods’. These results agree with what we observed in Fig. 2. There are more molecular commonalities between dermatitis, psoriasis, lupus, IBD, and possibly arthritis, than that of other diseases studied. We mapped signature disease genes from all disease categories to a single interactome and represented the number of interactions by gene label size, and the number of diseases it belongs to by node size (Fig. 5). We observed that, in Fig. 5I, many genes shared by multiple diseases (shown in yellow) contain more interactions with other genes, such as STAT1, EGR1, TLR2, CCL2, etc. However, it’s worth noting that genes unique to a disease can also hold a lot of interactions with other signature disease genes, such as STAT3, IL1B, CEBPB, IL10, MMP9, TLR4, EGF, etc. In Fig. 5II, we limited the signature disease genes to three indications: psoriasis, dermatitis, and COPD, which clearly shows their interactions across indications. For example, IL1B, a gene target of multiple drugs targeting different autoimmune diseases, is a signature disease gene for COPD. However, it does not only interact with multiple signature disease genes of COPD, but also directly interacts with many signature disease genes of psoriasis and dermatitis.Fig. 4

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