Thursday 13 November 2014

NHS Choices and Primary Sjogrens Syndrome

Updated pages on the NHS Choices website:

Thursday 23 January 2014

Proteins and PSS

Proteomic analysis of pooled blood serum samples to detect symptom-specific changes in primary Sjögren's Syndrome patients


Katherine James 1,2, Simon J Cockell 3, Colin S Gillespie 4, Anil Wipat 2, Jennifer Hallinan 2, Benedikt M Kessler 5, Roman Fischer 5, Simon J Bowman 6, Bridget Griffiths 7, UKPSSR Study Group 8

1 Musculoskeletal Research Group, Institute of Cellular Medicine, Newcastle University,

2 School of Computing Science, Newcastle University,

3 Bioinformatics Support Unit, Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University,

4 School of Mathematics and Statistics, Faculty of Science Agriculture and Engineering, Newcastle University,

5 Wellcome Trust Centre for Human Genetics and Henry Wellcome Building for Molecular Physiology, Nuffield Department of Medicine, University of Oxford,

6 Rheumatology Department, University Hospital Birmingham (Selly Oak), Birmingham,

7 Freeman Hospital, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne,

8 Institute of Cellular Medicine, Newcastle University


Objectives: Primary Sjögren's Syndrome (pSS) is a chronic autoimmune disease characterised by a range of symptoms including dryness, fatigue, depression, anxiety, pain and an increased risk of lymphoma. Patient populations are heterogeneous in their symptoms, making the accurate identification of pSS biomarkers non-trivial. This study aims to identify symptom-specific changes in protein abundance using serum samples from the UK Primary Sjögren's Syndrome Registry (UKPSSR), a cohort of clinically well-characterised pSS patients and healthy controls.


Methods: Patients were chosen from the UKPSSR database and grouped based on their levels of dryness, fatigue, depression, anxiety, and pain. Serum samples from each subject group were pooled and analysed by LC-MS/MS analysis using a Thermo LTQ Orbitrap Velos. Bioinformatic analyses and statistical modelling were then used to characterise the proteins and identify relationships between protein abundance and symptom levels.


Results: A total of 107 proteins were found to have significant changes in abundance between patient groups (ANOVA p>0.05, based on analytical duplicates). The majority of these proteins were immunoglobulins and components of the complement and coagulation cascades. Statistical modelling indicated that several of these abundance changes correlated with symptom levels. In particular, decreases in immunoglobulin chain regions are associated with the high fatigue and high pain groups, while slight decreases in complement components C1R and C4A were associated with dryness.


Conclusions: Differences in blood serum abundance of several proteins can be detected between groups of pSS patients with heterogeneous symptom profiles. These observations suggest that specific proteins may be biomarkers for the individual symptoms of pSS. Future analysis on a patient by patient basis could potentially reveal symptom-specific bio-fingerprints for individual symptom profiles.

Ranked List of Candidate Genes Associated with PSS

Application of probabilistic functional integrated network analysis to the study of autoimmunity and primary Sjögren's Syndrome


Katherine James 1,2, Jennifer Hallinan 2, Anil Wipat 2, Wan-Fai Ng 1

1 Musculoskeletal Research Group, Institute of Cellular Medicine, Newcastle University,

2 School of Computing Science, Newcastle University


Objectives: The majority of experimental datasets can be represented as networks of parts and interactions. A network representation allows biological data to be represented in a manner that is both tractable for human visual study and computationally amenable. One of the most powerful approaches to the integration of heterogeneous data is the use of probabilistic functional integrated networks (PFINs), since these networks have statistical weights that indicate the level of confidence in the evidence for each interaction. The confidence weights allow the use of a variety of statistical algorithms that take these weightings into account. This study aims to integrate an immune-specific PFIN from a number of relevant experimental datasets and apply it to the study of primary Sjögren's Syndrome (pSS) and related diseases.


Methods: Functional interaction data were sourced from the BioGRID and InnateDB resources. Datasets were confidence scored using a metabolic pathway Gold Standard dataset derived from the BioSystems Database. The confidence scores were integrated for each individual interaction using a weighted sum. The proteins of the network were then annotated using Gene Ontology biological process terms. Finally, the network was filtered to produce a sub-network of immune proteins and their high confidence interaction partners. The final network was assessed for its ability to predict known autoimmune disease-related proteins before being applied to the prediction of novel pSS-associated proteins and to the comparison of autoimmune diseases using a variety of network analysis techniques.


Results: A probabilistic functional integrated network of immunity was produced. The core immune PFIN contained ˜1700 proteins which were involved in >10,000 interactions. Clustering of the network based on interaction confidence revealed distinct patterns of interaction between pSS-associated proteins and several biological processes, in particular the stress responses. In addition, a ranked list of candidate pSS-associated genes was produced.


Conclusion: Probabilistic network analysis is a powerful approach to data integration and the study of human disease. The immune PFIN generated by this work provides a valuable resource for the future study of pSS and its comparison with other autoimmune diseases.

Monday 20 January 2014

Integrating Gene Expression and Clinical Data in Sjogren's Syndrome

Integration of gene expression data with functional interaction and annotation data reveals patterns of connection between pSS-associated genes and the cellular processes in which they are involved


Katherine James 1,2, Jessica R Tarn 1, Shereen Al-Ali 1, Jennifer Hallinan 2, David A Young 1, Wan-Fai Ng 1

1 Musculoskeletal Research Group, Institute of Cellular Medicine, Newcastle University,

2 School of Computing Science, Newcastle University


Objectives: There is considerable discordance in data from different gene expression studies of primary Sjögren's Syndrome (pSS). Combining these data with other types of information, such as functional interactions and annotation data, can provide a more complete view of the cell in order to identify the key genes and biological pathways that are involved in the disease process of pSS.


Methods: In this study, a list of genes, found to be differentially expressed between pSS patients and controls in four large-scale microarray studies, was derived from the literature. The enrichments of Gene Ontology (GO) biological process annotations for this list were calculated in order to identify those processes that may be involved in pSS pathogenesis.


BioGRID is a comprehensive and highly-curated resource for functional association data generated by multiple experimental techniques. Using BioGRID data, a functional interaction network was generated in which nodes represented genes or gene products, and edges represented any type of BioGRID interaction between the nodes. The network was visualised using the Cytoscape visualisation platform and further annotated based on the Gene Ontology enrichment results. Finally, the network was filtered to produce sub-networks of pSS-associated genes.


Results: Following filtering, a total of 99 of the pSS-associated genes were involved in 111 interactions in the sub-network, the majority of which were connected in one component of 88 genes. All four gene expression datasets were represented within this connected component. Several tight clusters between genes annotated to the processes "innate immune response", "multi-organism process", "response to virus" and "response to stress" were observed in the integrated network. The sub-network also revealed patterns of interaction between these clusters and the pSS-associated genes. In addition, a large number of the pSS-associated genes were found to be annotated to these GO biological processes.


Conclusion: Gene enrichment and network analyses of the pSS-associated genes suggest that the innate immune responses, multi-organism processes, and the responses to virus and to stress are likely to be involved in pSS pathogenesis. Integration of multiple types of data in this manner can aid in the interpretation of results since combining diverse data sources reveals global properties not evident from a single data source. Future studies may benefit from incorporating additional detailed clinical data during the analysis of expression data in order to elucidate the relationship between gene expression and clinical phenotype.