Differential protein network analysis of the immune cell lineage

Assigning protein function from domain-function associations using DomFun

BACKGROUND

Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions.

RESULTS

We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer's method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of [Formula: see text] and [Formula: see text] We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer's method led to the top performance in almost all scenarios.

CONCLUSIONS

DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer's method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun . Code maintained at https://github.com/ElenaRojano/DomFun . Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project .

Investigation of gamma secretase gene complex mutations in German population with Hidradenitis suppurativa designate a complex polygenic heritage

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic inflammatory disease affecting apocrine gland-bearing skin in the axilla, groin and under the breasts. Mutations of the gamma secretase gene complex, which is essential in the activation of Notch signalling pathways, were shown in some families with HS and in a few sporadic cases. Although an imbalance in Notch signalling is implicated in the pathogenesis, the exact mechanism of HS development is yet unknown.

OBJECTIVES

We aim to investigate the genetic basis of HS by determining the presence of mutations of gamma secretase gene complex in a cohort of HS patients and by searching for a disease-causing pathogenic variant in a multi-generational HS family using parametric linkage analysis.

METHODS

Thirty-eight patients clinically diagnosed with HS were included in this study. All exons and exon-intron boundaries of the genes encoding gamma secretase complex consisting of six genes: APH1A, APH1B, PSENEN, NCSTN, PSEN1 and PSEN2 were sequenced by Sanger technique. Genetic mapping with parametric linkage analysis for the patients in the family was performed with eight affected and four healthy individuals. The logarithm of odds was calculated.

RESULTS

In a sporadic patient with early-onset, severe lesions in axilla and groin, a novel single-nucleotide deletion causing frameshift in exon 1 of the NCSTN gene was identified ((NM_015331.3): c.38delG, p.(Gly13Glufs*15)). The LOD score of 1.5 was never exceeded in any region of the genome, pointing towards intricate multi-genic inheritance pattern within the affected family.

CONCLUSIONS

The gamma secretase gene complex mutations were rare in our cohort (3.2%). Besides, our analysis indicates a possible complex multi-genic inheritance in a seemingly autosomal dominantly inherited large HS family. Genetics of both familial and sporadic HS may be complicated in most cases, and the role of other potential genes such as autoinflammatory and modifier genes as well as environmental factors may influence the pathogenesis.

A novel nicastrin mutation in a three-generation Dutch family with hidradenitis suppurativa: a search for functional significance

Background

Mutations in the γ‐secretase enzyme subunits have been described in multiple kindreds with familial hidradenitis suppurativa (HS).

Objective

In this study, we report a novel nicastrin (NCSTN) mutation causing HS in a Dutch family. We sought to explore the immunobiological function of NCSTN mutations using data of the Immunological Genome Project.

Methods

Blood samples of three affected and two unaffected family members were collected. Whole‐genome sequencing was performed using genomic DNA isolated from peripheral blood leucocytes. Sanger sequencing was done to confirm the causative NCSTN variant and the familial segregation. The microarray data set of the Immunological Genome Project was used for thorough dissection of the expression and function of wildtype NCSTN in the immune system.

Results

In a family consisting of 23 members, we found an autosomal dominant inheritance pattern of HS and detected a novel splice site mutation (c.1912_1915delCAGT) in the NCSTN gene resulting in a frameshift and subsequent premature stop. All affected individuals had HS lesions on non‐flexural and atypical locations. Wildtype NCSTN appears to be upregulated in myeloid cells like monocytes and macrophages, and in mesenchymal cells such as fibroblastic reticular cells and fibroblasts. In addition, within the 25 highest co‐expressed genes with NCSTN we identified CAPNS1,ARNT and PPARD.

Conclusion

This study reports the identification a novel NCSTN gene splice site mutation which causes familial HS. The associated immunobiological functions of NCSTN and its co‐expressed genes ARNT and PPARD link genetics to the most common environmental and metabolic HS risk factors which are smoking and obesity.

Profiling networks of distinct immune-cells in tumors

Background

It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences.

Results

To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling.

Conclusions

This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1141-3) contains supplementary material, which is available to authorized users.