Disease Study

Sjögren’s Syndrome

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Sjögren’s Syndrome

Methodology

The PrecisionLife® platform is a massively scalable multi-omics association platform that enables the hypothesis-free detection of high-order, phenotype-associated combinations at genome-wide study scale. It can find and statistically validate combinations of multiple features (typically 3 to 10 features in combination, known as “signatures”) that together are strongly associated with a specific disease diagnosis or other clinical phenotype. The underlying analytical mining platform has been validated in multiple disease populations.3

We analyzed a dataset that was generated from the UK Biobank,4 consisting of 990 cases and 1,969 controls with genotype data on 547,197 SNPs. Cases with Sjögren’s syndrome (both primary and secondary) were identified using the ICD-10 code M350 and UK Biobank field 20002. An age- and gender-matched control set was generated consisting of randomly selected individuals who did not have any reported eye disorders and were not diagnosed with any auto-immune disease. The PrecisionLife platform took less than two days to identify the combinatorial genomic signatures in this dataset on a dual Xeon, 4x-GPU compute server.

The identified disease signatures were used to explore disease mechanisms and identify novel disease targets. The disease-associated SNPs were mapped to the human reference genome in order to identify disease-associated and clinically relevant target genes. A semantic knowledge graph derived from multiple public and private data sources was used to annotate the targets, testing the 5Rs criteria of early drug discovery5 and forming strong, testable hypotheses for their mechanism of action and impact on the disease phenotype.

We also applied a set of heuristics to the risk-associated genes in order to identify novel disease targets, and targets with high small-molecule drug development potential. These criteria included a strong association of the target with the disease and its pathophysiological mechanisms, relevant tissue expression, and tractability for novel disease targets, among several others.

Additional criteria used for prioritizing repurposing targets included known drugs, patent scope, and favorable pharmacokinetic and toxicology profiles for repurposing targets. All targets have detailed genetic signatures that can form the basis of patient stratification biomarkers due to the analytical methodology used.

Executive Summary

Sjögren’s syndrome is an auto-immune disease affecting 0.1–3% of the population, with women 20 times more likely to develop symptoms characterized by dry eyes and dry mouth.1 However, the disease is highly heterogeneous, with patients also presenting with a wide range of extraglandular symptoms, and Sjögren’s syndrome is often associated with other auto-immune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE).2 There is a clear need to better stratify Sjögren’s patients into more clinically relevant subtypes, in order to develop a more personalized approach to treatment.

We analyzed genotype data from 990 Sjögren’s cases obtained from the UK Biobank and identified over 1,800 different disease-associated combinations of single nucleotide polymorphisms (SNPs). These combinations were then clustered, revealing 23 different patient subpopulations (or communities) that may be used to better distinguish and explain the underlying disease mechanisms.

However, genotype–phenotype correlation analysis of the patient communities was not possible due

to the lack of available relevant phenotype data and the limitations of the in-patient data associated with the dataset. In part, this includes a lack of clarity in primary vs. secondary Sjögren’s syndrome diagnoses, and many self-reported cases.

Nevertheless, we were still able to identify over 299 significant gene targets within these subpopulations that are involved in a variety of different mechanisms implicated in Sjögren’s syndrome, including Wnt/ β-catenin signaling, T-cell-mediated auto-immunity, and endoplasmic reticulum (ER) stress and apoptosis.

Many of the highest-scoring genes have already been investigated in the context of Sjögren’s or another auto-immune disease. As patients appear to stratify according to several of these disease mechanisms, this could help explain the apparent heterogeneity of the patient population observed in the clinic. Furthermore, as several of these targets have known active chemistry, they could represent potential novel drug target opportunities.

© PrecisionLife Ltd 2021 All rights reserved | 3

Figure 1 Disease architecture of Sjögren’s syndrome generated by the PrecisionLife platform. Each color identifies a distinct patient subpopulation group or community (23 in total).

Results

Our analysis produced combinatorial risk signatures that can be used to accurately explain disease mechanisms, and to identify clinically relevant target genes that can be used for novel drug development and biomarker discovery.

This study identified more than 7,000 disease signatures representing different combinations of SNPs within the Sjögren’s patient population. These signatures consisted of 2–5 SNP genotypes in combination, which would not have been discovered using standard Genome-Wide Association Studies (GWAS). These SNP signatures were then clustered based on their co-occurrence in patients, which generated a complex disease architecture of

23 distinct patient subpopulations or communities (see Figure 1).

We found 299 risk-associated genes with SNP variants in them that are strongly associated with Sjögren’s syndrome. Some of these gene targets have already been demonstrated to be biological drivers of Sjögren’s pathogenesis in scientific literature, providing validation for our hypothesis-free approach to analyzing disease populations. However, many of the genes we identified in our analysis represent novel targets that have a strong mechanistic link to auto-immunity, but have not yet been studied in the context of Sjögren’s.

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Top-Scoring Genes Can be Mechanistically Linked to Sjögren’s Syndrome

We found 299 risk-associated genes that are strongly associated with Sjögren’s syndrome. While several of the highest-scoring genes have already been implicated in driving other auto-immune diseases, either through

regulation of the immune response or as the target of auto-antibodies, others encode more novel targets. These are involved in mechanisms such as cell adhesion, ER stress, and autophagy.

Gene Physiological Function Link to Sjögren’s Syndrome

GENE 1Interferon regulatory factor,

transcriptionally regulates a range of inflammatory genes

GENE 1 has been associated with a whole range of auto-immune conditions, including SLE, RA, multiple

sclerosis (MS), and Sjögren’s. Knockout mice are resistant to developing RA and SLE, and have significant

lower levels of a range of pro-inflammatory cytokines.

GENE 2Novel autophagy target, plays a role

in autophagosome biogenesis

Several studies have also demonstrated dysregulation of autophagy in Sjögren’s syndrome and a variety of

other auto-immune diseases.6, 7 Moreover, a recent study indicated that autophagy was increased in T lymphocytes

in the salivary glands of Sjögren’s patients, and this increase correlated with increased expression of

pro-inflammatory cytokines such as IL-23.8

GENE 3Encodes a macrophage scavenger receptor-like protein, protects cells

from oxidative stress

Auto-antibodies against GENE 3 have been found in SLE and Sjögren’s syndrome. GENE 3 is also involved

in regulating peripheral tolerance.

GENE 4Encodes a GTPase, forming

a component of the spliceosome complex

Knockout of GENE 4 resulted in decreased production of pro-inflammatory cytokines and a reduction in chronic intestinal inflammation. GENE 4 has also been found to

be a modulator of innate immunity in mouse macrophages, with reduced expression associated

with decreased IL-6 and TNF-α production.

GENE 5

Regulates the immune response by translationally silencing

pro-inflammatory molecules such as CCL3, CCL22, and CCL11

GENE 5 knockout mice had worsened symptoms in a model of ulcerative colitis.

CCL3, CCL22, and CCL11 are all increased in patients

with Sjögren’s syndrome.9,10,11 Furthermore, higher CCL11 levels are associated with increased risk of

developing non-Hodgkin’s lymphoma.

GENE 6Novel cell–cell adhesion and

morphology gene; high expression is found in the gastrointestinal tract

Loss of GENE 6 expression causes loss of cell adhesion and cell death (in kidney epithelial cells).

GENE 7Highly novel target, may be

involved in the ER stress response and regulation of protein folding

Patients with Sjögren’s syndrome present with higher levels of ER stress in the salivary glands.12

GENE 8Encodes an ADP-ribose

pyrophosphatase; critical for activation of TRMP2

TRMP2 has been shown to drive auto-immune diseases by increasing T-cell proliferation and

pro-inflammatory cytokine secretion.13 Inhibition of TRMP2 channels attenuated experimental auto-immune

encephalitis (EAE) pathogenesis.14

GENE 9Transglutaminase that plays a

role in protein cross-linking Anti-GENE 9 antibodies have been found in patients

with celiac disease and progressive MS.

GENE 10Inhibitor of phospholipase A2 and

has anti-inflammatory activityA classic auto-immune target that has been studied

in multiple diseases, including RA and SLE.

Table 1 List of the top-scoring genes associated with Sjögren’s risk and their mechanism of action

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Patient Clustering Based on Common SNP Variants with Similar Underlying Disease Mechanisms

Our study identified 299 disease signatures representing different combinations of disease-associated SNPs within the Sjögren’s patient population. When these SNP networks were clustered based on their co-occurrence in patients, we generated 23 distinct patient communities (see Figure 1). Functional analysis of these communities indicates that patients can be clustered based on differences in underlying disease mechanisms.

Community 2

The six highest-scoring genes found in Community 2 are involved in the Wnt/β-catenin signaling pathway. Although this pathway is primarily involved in cell–cell signaling and has classically been studied in the context of carcinogenesis, studies have found Wnt/β-catenin activation plays a role in mucosal tolerance, protecting against the development of auto-immunity.15 Furthermore, in a mouse model of MS, activation of the canonical β-catenin pathway reduced disease severity and resulted in decreased levels of Th1/Th17-related cytokines.16 In Sjögren’s syndrome, a recent publication found four polymorphisms in genes relating to this pathway that were associated with the risk of developing primary Sjögren’s.17

We found six gene targets, including some with active chemistry, that can be linked to this pathway, indicating that dysregulation of Wnt/β-catenin signaling drives the pathophysiology of a subset of patients with Sjögren’s.

Community 7

Several of the genes found in Community 7 encode activators of the adaptive immune system. We also found a cluster of SNPs relating to the HLA class II block in this community. These genes are all functionally involved in the adaptive immune response and auto-immunity, with roles such as T-cell transcriptional regulation and induction of cytokine-withdrawal induced T-cell apoptosis. Moreover, several of these genes have been

implicated in other auto-immune diseases, including MS and SLE.

Community 9

In Community 9 we found six of the highest-scoring genes encoding proteins with functions all relating to epithelial cell adhesion, and extracellular matrix (ECM) and basement membrane integrity. Various studies have shown that there are distinct changes in the ECM in both the salivary and lacrimal glands in Sjögren’s patients,18, 19

as well as deterioration in collagen protein expression and structure in salivary tissue in mouse models of the disease.These changes have been hypothesized to create an environment that allows infiltration of inflammatory cells driving auto-immunity in addition to inhibiting normal glandular function.

Community 17

Finally, we found a distinct set of SNPs corresponding to five genes found in Community 17 that are directly implicated in ER stress and apoptosis mechanisms. Although none of them have been investigated in the context of Sjögren’s syndrome and can therefore be considered novel targets, there is clear evidence that both increased ER stress and apoptosis are associated with Sjögren’s syndrome pathogenesis.

A study has shown that ER stress is increased in salivary epithelial cells from Sjögren’s patients, resulting in increased expression of the classic Sjögren’s Ro/SSA and La/SSB auto-antigens on the surface of epithelial cells in the salivary glands.20 Furthermore, increased epithelial apoptosis may result in impaired lacrimal and salivary secretions, contributing to the classic symptoms displayed by Sjögren’s syndrome patients.21 Higher rates of apoptosis may be due to inherent imbalances in pro- and anti-apoptotic factors, or as a result of auto-antibodies triggering apoptotic processes.22

Mapping Drugs to Key Genes Facilitates the Rapid Identification of Repurposing Candidates

In addition to gaining greater insights into the underlying disease processes in Sjögren’s syndrome, we can also use our platform to identify drug repurposing candidates for key disease-associated targets. Using our platform, we can map existing drug options onto the genes found in each community of the patient population (Figure 2), enabling us to rapidly identify candidates that may be therapeutically beneficial for select subsets of the patient population. Applying the series of heuristics we have developed allows us to efficiently prioritize those with the greatest repurposing potential for further investigation.

Among a range of novel repurposing leads, we also found several examples of targets with drugs that already been shown to protect against the development of auto-immune diseases in a range of mouse models. Although some of these genes are directly involved in T-cell-mediated auto-immunity and pro-inflammatory cytokine signaling, others are implicated in epithelial cell integrity and neurotransmission.

Figure 2 Graph showing SNP signatures (blue), risk-associated genes (pink), and existing drug options (yellow) in a community within the Sjögren’s syndrome disease population

© PrecisionLife Ltd 2021 All rights reserved | 6

These existing drugs are also useful as active chemical starting points that can be used in the development of novel small-molecule therapies, having been

optimized for better selectivity and pharmacokinetic properties.

Conclusion

The current analysis was performed on a genotype dataset of 990 UK Biobank patients with Sjögren’s syndrome in an entirely hypothesis-free manner, and without use of any phenotype data.

We identified 23 distinct patient populations containing over 200 risk-associated genes, from which there are many potential new drug targets. We have also found many different disease mechanisms associated with these 23 communities that could help to provide greater

insights into the heterogeneous nature of Sjögren’s syndrome.

As additional data becomes available, more detailed stratification and analysis of the patient population will be possible using a combination of genomic and phenotypic features. This will enable development of more detailed insights and personalized medicine strategies for Sjögren’s syndrome patients.

Notes and References

1. Maciel, G., Crowson, C., Matteson, E., & Cornec, D. (2017).FRI0278 Prevalence of primary Sjögren’s syndrome in a population-based cohort in the United States. Annals of the Rheumatic Diseases,76, 591

2. Hammitt, K. M., Naegeli, A. N., van den Broek, R., & Birt,

J. A. (2017). Patient burden of Sjögren’s: a comprehensive literature review revealing the range and heterogeneity of measures used in assessments of severity. RMD Open, 3(2), e000443. https://doi.org/10.1136/rmdopen-2017-000443

3. Mellerup, E., Andreassen, O. A., Bennike, B., Dam, H., Djurovic, S., Jorgensen, M. B., Kessing, L. V., Koefoed, P., Melle, I., Mors, O., & Møller, G. L. (2017). Combinations of genetic variants associated with bipolar disorder. PloS One, 12(12), e0189739. https://doi.org/10.1371/journal.pone.0189739

4. Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., Cortes, A., Welsh, S., Young, A., Effingham, M., McVean, G., Leslie, S., Allen, N., Donnelly, P., & Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-z

5. Morgan, P., Brown, D. G., Lennard, S., Anderton, M. J., Barrett, J. C., Eriksson, U., Fidock, M., Hamrén, B., Johnson, A., March, R. E., Matcham, J., Mettetal, J., Nicholls, D. J., Platz, S., Rees, S., Snowden, M. A., & Pangalos, M. N. (2018). Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nature Reviews. Drug Discovery, 17(3), 167–181. https://doi.org/10.1038/nrd.2017.244

6. Byun, Y. S., Lee, H. J., Shin, S., & Chung, S. H. (2017). Elevation of autophagy markers in Sjögren syndrome dry eye. Scientific Reports, 7(1), 17280. https://doi.org/10.1038/s41598-017-17128-0

7. Li, B., Wang, F., Schall, N., & Muller, S. (2018). Rescue of autophagy and lysosome defects in salivary glands of MRL/lpr mice by a therapeutic phosphopeptide. Journal of Autoimmunity, 90, 132–145. https://doi.org/10.1016/j.jaut.2018.02.005

8. Alessandri, C., Ciccia, F., Priori, R., Astorri, E., Guggino, G., Alessandro, R., Rizzo, A., Conti, F., Minniti, A., Barbati, C., Vomero, M., Pendolino, M., Finucci, A., Ortona, E., Colasanti, T., Pierdominici, M., Malorni, W., Triolo, G., & Valesini, G. (2017). CD4 T lymphocyte autophagy is upregulated in the salivary glands of primary Sjögren’s syndrome patients and correlates with focus score and disease activity. Arthritis Research & Therapy, 19(1), 178. https://doi.org/10.1186/s13075-017-1385-y

9. Choi, W., Li, Z., Oh, H. J., Im, S. K., Lee, S. H., Park, S. H., You, I. C., & Yoon, K. C. (2012). Expression of CCR5 and its ligands CCL3, -4, and -5 in the tear film and ocular surface of patients with dry eye disease. Current Eye Research, 37(1), 12–17. https://doi.org/10.3109/02713683.2011.622852

10. Ushio, A., Arakaki, R., Otsuka, K., Yamada, A., Tsunematsu, T., Kudo, Y., Aota, K., Azuma, M., & Ishimaru, N. (2018). CCL22-Producing Resident Macrophages Enhance T Cell Response in Sjögren’s Syndrome. Frontiers in Immunology, 9, 2594. https://doi.org/10.3389/fimmu.2018.02594

11. Nocturne, G., Seror, R., Fogel, O., Belkhir, R., Boudaoud, S., Saraux, A., Larroche, C., Le Guern, V., Gottenberg, J. E., & Mariette, X. (2015). CXCL13 and CCL11 Serum Levels and Lymphoma and Disease Activity in Primary Sjögren’s Syndrome. Arthritis & Rheumatology, 67(12), 3226–3233. https://doi.org/10.1002/art.39315

12. Katsiougiannis, S., Tenta, R., & Skopouli, F. N. (2015). Endoplasmic reticulum stress causes autophagy and apoptosis leading to cellular redistribution of the autoantigens Ro/Sjögren’s syndrome-related antigen A (SSA) and La/SSB in salivary gland epithelial cells. Clinical and Experimental Immunology, 181(2), 244–252. https://doi.org/10.1111/cei.12638

13. Melzer, N., Hicking, G., Göbel, K., & Wiendl, H. (2012). TRPM2 cation channels modulate T cell effector functions and contribute to autoimmune CNS inflammation. PloS One, 7(10), e47617. https://doi.org/10.1371/journal.pone.0047617

14. Tsutsui, M., Hirase, R., Miyamura, S., Nagayasu, K., Nakagawa, T., Mori, Y., Shirakawa, H., & Kaneko, S. (2018). TRPM2 Exacerbates Central Nervous System Inflammation in Experimental Autoimmune Encephalomyelitis by Increasing Production of CXCL2 Chemokines. The Journal of Neuroscience, 38(39), 8484–8495. https://doi.org/10.1523/JNEUROSCI.2203-17.2018

15. Suryawanshi, A., Tadagavadi, R. K., Swafford, D., & Manicassamy, S. (2016). Modulation of Inflammatory Responses by Wnt/β-Catenin Signaling in Dendritic Cells: A Novel Immunotherapy Target for Autoimmunity and Cancer. Frontiers in Immunology, 7, 460. https://doi.org/10.3389/fimmu.2016.00460

16. Suryawanshi, A., Manoharan, I., Hong, Y., Swafford, D., Majumdar, T., Taketo, M. M., Manicassamy, B., Koni, P. A., Thangaraju, M., Sun, Z., Mellor, A. L., Munn, D. H., & Manicassamy, S. (2015). Canonical wnt signaling in dendritic cells regulates Th1/Th17 responses and suppresses autoimmune neuroinflammation. Journal of Immunology, 194(7), 3295–3304. https://doi.org/10.4049/jimmunol.1402691

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Notes and References continued

17. Fernández-Torres, J., Pérez-Hernández, N., Hernández-Molina, G., Martínez-Nava, G. A., Garrido-Rodríguez, D., López-Reyes, A., & Rodríguez-Pérez, J. M. (2020). Risk of Wnt/β-catenin signalling pathway gene polymorphisms in primary Sjögren’s syndrome. Rheumatology, 59(2), 418–425. https://doi.org/10.1093/rheumatology/kez269

18. Sisto, M., D’Amore, M., Lofrumento, D. D., Scagliusi, P., D’Amore, S., Mitolo, V., & Lisi, S. (2009). Fibulin-6 expression and anoikis in human salivary gland epithelial cells: implications in Sjogren’s syndrome. International Immunology, 21(3), 303–311. https://doi.org/10.1093/intimm/dxp001

19. Schenke-Layland, K., Xie, J., Angelis, E., Starcher, B., Wu, K., Riemann, I., MacLellan, W. R., & Hamm-Alvarez, S. F. (2008). Increased degradation of extracellular matrix structures of lacrimal glands implicated in the pathogenesis of Sjögren’s syndrome. Matrix Biology, 27(1), 53–66. https://doi.org/10.1016/j.matbio.2007.07.005

20. Katsiougiannis, et al. Endoplasmic reticulum stress causes autophagy and apoptosis leading to cellular redistribution of the autoantigens Ro/Sjögren’s syndrome-related antigen A (SSA) and La/SSB in salivary gland epithelial cells.

21. Manganelli, P., & Fietta, P. (2003). Apoptosis and Sjögren syndrome. Seminars in Arthritis and Rheumatism, 33(1), 49–65. https://doi.org/10.1053/sarh.2003.50019

22. Sisto, M., Lisi, S., Lofrumento, D., D’Amore, M., Scagliusi, P., & Mitolo, V. (2007). Autoantibodies from Sjögren’s syndrome trigger apoptosis in salivary gland cell line. Annals of the New York Academy of Sciences, 1108, 418–425. https://doi.org/10.1196/annals.1422.044