Molecular Biology Approaches to Understanding Spondyloarthritis

Introduction

Numerous factors involved with the pathogenesis of spondyloarthritis (SpA) from genetic predisposition, transcriptional expression of genes, immune function, and environmental factors like the microbiome are increasingly identified as potential contributors to the pathophysiology. Recent advances in molecular techniques, including ‘omics technologies and improved immune profiling methods have allowed for further elucidation of important pathways and cell types, expanding our understanding of diseases like ankylosing spondylitis (AS) and psoriatic arthritis (PsA). Here we describe some of the molecular approaches being utilized as well as some of the recent advances that are helping to progress the field and provide more targets for intervention.

Genetics

The genetic landscape has evolved over the past 5 years away from traditional Mendelian approaches towards genome wide association studies (GWAS), next-generation sequencing, and epigenetics. These new molecular approaches have led to significant advances in science, and have provided a powerful platform for studies in SpA, extending our genetic understanding of SpA well beyond HLA-B27, which only contributes 20.1% of the heritability of AS¹.

GWAS and Next-Generation Sequencing

Genome wide association studies (GWAS) are a complex genetic analysis tool that allows for the detailed profiling of genetic variants. GWAS allows for the analysis of hundreds of thousands of single nucleotide polymorphisms (SNPs) to be assessed for association with a given disease². GWAS is most commonly performed as a case-control study design with a group of patients diagnosed with a disease of interest compared to healthy controls. For this type of study, DNA is isolated usually from peripheral blood and subsequently genotyped using commercial chip platforms. After data quality control, which can be assess by several computational programs, data are analyzed for associations between SNPs and disease. Generally an analysis is followed by replicating associations in an independent population sample³.

GWAS have been utilized extensively in SpA to identify genes linked to signaling pathways not previously identified^4–6. This is further exemplified nicely in the study by Ellinghaus et al. in which 86,000 individuals of European ancestry with AS, inflammatory bowel diseases, primary sclerosing cholangitis, and psoriasis were compared to healthy controls⁷. The analysis allowed identification of 27 new genetic susceptibility loci, 17 in AS alone, and demonstrated shared risk among these related diseases. Studies like this have identified novel pathways of disease based on susceptibility loci such as IL23R, IL12B, and CCR6, which affect CD4+ effector function⁸. Numerous other genes related to the IL-23/IL-17 pathway have also been identified through GWAS, including, CARD9, PTGER4, TYK2, and STAT3⁸ as well as ERAP1 and ERAP2, which relate to peptide trimming in the ER for peptide loading onto HLA class 1 molecules such as HLA-B27⁹. While these data have vastly expanded our understanding of genetic susceptibility for SpA, GWA studies are limited by their sample size and heterogeneity. They best identify common genetic variants, but can miss rare polymorphisms and are unable to identify genetic interactions with other loci.

Better identification of rare genetic variants of clinical importance can be achieved through next generation sequencing (NGS) methodologies like whole exome and genome sequencing. In these methods, DNA is extracted from white blood cells, broken into short fragments, and the DNA sequence determined through various sequencing technologies¹⁰. These fragments are then read as millions of short sequencing DNA reads, followed by alignment to the human genome reference sequence via a variety of available computational tools. In this manner DNA composition can be determined in a specific order with individual nucleotides. Overall NGS is a valuable tool for detecting single nucleotide substitutions and/or insertions, as well as differences in gene composition¹⁰. One study of exosome sequencing in AS confirmed previously identified susceptibility polymorphisms such as ERAP1 and IL23R; however the study was underpowered to identify novel rare variants¹¹.

Epigenetics: Methylation and Histone acetylation/deacetylation

DNA methylation is an epigenetic modification in which methyl groups are added to cysteine or adenine residues thereby controlling transcription¹². Age, sex, smoking, medications, alcohol, and diet are known to affect DNA methylation¹³. Methylation patterns have been implicated in numerous biologic processes such as aging, and a number of diseases including SpA¹². DNA methylransferase 1 (DNMT1) is an enzyme that regulates patterns of methylated cytosine residues. Expression of DNMT1 is decreased in the setting of increased methylation of the DNMT1 promoter in AS patients compared to healthy controls, which is of unclear etiology in AS pathogenesis as this did not correlate with clinical manifestations¹⁴. Furthermore, numerous genes in AS have been found to be differentially methylated, with hypermethylation of HLA-DQB1 having the most significant signal¹⁵. Another gene, B-cell chronic lymphocytic leukemia/lymphoma 11B (BCL11B) was also found to have increased methylation and decreased transcription in AS compared to healthy controls¹⁶. More recently, the first study to assess the role of HLA-B27 in methylation status of AS was performed. This study found hypomethylation of HCP5, tubulin folding cofactor A, and phospholipase D Family Member 6 in AS patients¹⁷. Thus, methylation studies have identified additional genes that may be important in disease development.

Histone modification allows activation (euchromatin) and deactivation (heterochromatin) of chromatin by histone acetyltransferases (HAT) and histone deacetylases (HDAC)¹⁸. These different acetylation patterns allow for chromatin stability, gene regulation, and transcription silencing. The concept of histone modification has been minimally studied in SpA. In peripheral blood mononuclear cells (PBMCs) from patients with AS, HAT and HDAC activity were significantly reduced compared to healthy controls¹⁹. HDAC inhibitors such as sirtinol were able to decrease HDAC expression in healthy controls, but not in AS. Sirtinol decreased production of TNF in AS patients, suggesting an intriguing treatment strategy in AS requiring further study¹⁹. Targeting modifiable states such as histone acetylation versus unmodifiable states such as genetic composition is an attractive strategy that will likely be studied further in the future. For example, HDAC inhibitors have shown anti-inflammatory effects in vitro in rheumatoid arthritis models^20,21, but have not been studied thus far in SpA.

Micro RNA

Micro RNA (miRNA) is a type of small, non-coding RNA of ~22 nucleotides that can form complex networks that regulate cell differentiation, development, and homeostasis²². MiRNAs have been proposed to be involved in the pathogenesis of numerous rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and osteoarthritis^23–25. The role of miRNA in the pathogenesis of AS has indicated a variety of expression variability of miRNAs. Common miRNA profiles in AS have identified individuals with increased miR-146a and miR-155²⁶. Mechanistically, this has been studied in terms of gene regulation, in that miR-146a has been shown to inhibit dickkopf 1 (DKK1), which has also been implicated in AS pathogenesis, and miR-146a knockdown models can hinder AS progression²⁷. Other miRNAs that have been implicated in AS include miR-125a-5p, miR-151a-3p, and miR-22–3p based upon higher expression compared to healthy controls²⁸.

Given that there is increased specific miRNA expression in patients with AS compared to healthy controls, miRNAs represent a potential biomarker for diagnosis, disease activity, or potential therapeutic targets. The previously mentioned miRNAs miR-146a and miR-155 have been found to correlate with disease activity using Bath Ankylosing Spondylitis Disease Activity Index (BASDAI)²⁶. Additional correlations with BASDAI and miR-625–3p and miR-29a also have been identified^29,30. Other miRNAs have been identified in AS patients with downregulated miR-199a-5p correlating with increased TNF, IL-and IL-23³¹. This finding implies that miRNAs could distinguish SpA phenotypes responsive to inhibitors of TNF, IL-17, or IL-23³². This concept has also been assessed in PsA with specific miRNA signatures noted in patients with PsA compared to healthy controls, which have been proposed as biomarkers or potential therapeutic targets³³.

Proteomics and Metabolomics

Proteomics and metabolomics are “omics” tools that focus on unbiased identification and measurement of the proteins or small molecule metabolites in a biologic system. These approaches rely on high-pressure liquid chromatography and mass spectrometry of a sample followed by alignment of results with a database of known proteins and metabolites for identification. Addition of known concentrations of specific proteins and metabolites allows further quantification within samples. These approaches have potential to identify biomarkers for disease diagnosis and activity as well as pathogenic mechanisms. For example, proteomics identified several candidate biomarkers for either the diagnosis of or conversion from psoriasis to PsA^34–36; however, none of these candidates have been confirmed in independent cohorts.. Similarly, in AS, two proteomic studies suggested biologic processes of cytotoxicity and vitamin D binding in the pathogenesis of AS^37,38, although these have yet to be validated. With regards to metabolomics, analysis of multiple tissues including plasma, urine, and hip ligament found alterations in fat, glucose, and choline metabolism^39,40. Given the known dysbiosis in the fecal microbiota that has been identified in SpA compared to controls^41–43, metabolites have also been studied in fecal samples in a pediatric population with enthesitis-related arthritis. This investigation found decreased metabolic diversity and alterations in the tryptophan metabolism pathway relative to healthy controls⁴⁴.

Microbiome

Alterations in intestinal bacterial communities (dysbiosis) have been identified in SpA, though the etiology and consequence of dysbiosis is not elucidated^41–43. The microbiome represents an emerging area in SpA given the presumed gut-joint relationship in disease pathogenesis. There are numerous approaches towards studying the microbiome in patients with SpA: 16S sequencing identifies bacteria through limited sequencing of regions within the bacterial 16S ribosomal RNA gene and alignment with sequence databases; this approach limits identification of bacteria to the level of genera. Similar methods exist for sequencing fungi using the internal transcribed spacer (ITS). Shotgun metagenomics allows species-level identification through untargeted sequencing using random primer sets. This technique will also provide the functional potential of a community after analysis of genetic pathways identified by sequencing. Metatranscriptomics and sc RNA-seq are still being optimized to microbiome analysis and will provide another level of analysis.

A limitation of microbiome studies to date in AS have been the variability of findings from one study cohort to another^41,43,45. For example, Breban et al. identified a significant expansion of Ruminococcus gnavus by 16S sequencing of stool in patients with AS comparted to controls⁴³. However, Tito et al. identified a significant expansion of Dialister by 16S sequencing of intestinal biopsies from individuals with AS that correlated with disease activity scores⁴⁶. The variation of findings may be related to tissue sampling (e.g. stool versus mucosa-associated bacteria), geographic differences, or other confounding factors that need to be considered when designing and interpreting microbiome studies in AS. Furthermore, the specific microbiota may not be as important as the community function, which may be better assessed with newer technologies such as metagenomics and metatranscriptomics.

Immune phenotyping

Numerous new techniques have been utilized in discovering novel immune phenotypes in SpA. Traditional immune cell profiling through flow cytometry has led to the identification of cells hypothesized to contribute to disease pathogenesis. These include expanded Th22, Th17, γδ T, and MAIT cells in the peripheral blood^47–50 as well as IL-17 producing NK cells in the intestine of AS patients⁵¹. As general knowledge of immune cells and function expand, traditional techniques have revealed additional cell types, such as innate lymphoid cells (ILCs). Increased levels of type 3 innate lymphoid cells (ILC3s) that produce IL-17 and IL-22 have been identified in the intestine, peripheral blood, synovial fluid, and bone marrow of AS patients⁴⁸, and in the peripheral blood of patients with PsA that correlate with clinical disease activity⁵². Newer molecular approaches, namely mass cytometry and single cell RNA sequencing, have additive potential to reveal unique cell types and pathways.

CyTOF and Imaging Mass Cytometry

Traditional flow cytometry is limited by use of fluorescent dyes that have overlapping spectra, thus limiting the resolution between dyes and number of antibody markers that can be combined (usually <20). Cytometry by Time-of-Flight (CyTOF) is a technology for single cell analysis that relies upon using heavy metal ions as antibody labels without the limitations of fluorescence⁵³, allowing combinations of greater numbers of antibodies, upwards of ~40. Recently CyTOF was used to identify an expansion of unique CD8+ T cells in the synovial fluid of patients with AS. These CD8+ T cells expressed integrins β7, CD103, CD29 and CD49a⁵⁴. Thus, CyTOF has the power to more thoroughly characterize immune cell populations. Coupling CyTOF technology with histology, imaging mass cytometry has the ability to add spatial information, which will be a powerful tool in understanding the function of immune cells in tissues relevant to SpA.

Single cell RNA-Seq and CITE-Seq

Single cell RNA-sequencing (scRNA-seq) allows even more refined profiling of immune cells from patients with SpA. There are numerous different methods, each with specific strengths and weaknesses, for single cell isolation followed by amplification and next generation sequencing of transcribed RNA as reviewed by Papalexi and Satija⁵⁵. Analysis of the data allows for characterization of distinct cell subsets, uncovering the heterogeneity within a population and dissecting cell fate branch points⁵⁵. The power of scRNA-seq is best highlighted by the efforts of the Accelerating Medicines Partnership in profiling peripheral blood and tissue cells in patients with RA and SLE⁵⁶, which has revealed novel cellular functions for further study in the pathogenesis of these diseases. A significant limitation of scRNA-seq is that immune cells are characterized by cell surface proteins that are not highly expressed mRNAs, and thus not easily detected through transcriptional sequencing. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) adds upon scRNA-seq through the use of nucleotide barcode labeled antibodies targeting cell surface markers. These barcodes are sequenced along with the transcriptome of each cell, allowing the coupling of protein marker information in the analysis of cellular populations. Although studies utilizing scRNA-seq in SpA have yet to be published, this represents an approach that could lead to significant advances in the field of SpA in terms of pathophysiology, diagnosis, and pharmacology.

Integrated ‘Omics

With the rise of multiple “omics” technologies, integration of data from these approaches represents a powerful analysis for understanding aspects of SpA. This method can be used to study pathophysiology, as well as identify candidate biomarkers for diagnostic purposes. For example, a recent study in inflammatory bowel disease utilized the techniques of multi-omics to study microbial dysbiosis with regards to host factors towards dysregulation of microbial transcription, metabolite pools, and levels of antibodies in the serum⁵⁷. A similar approach was used in the rat HLA-B27 transgenic model of AS in which microbiome analysis and host bulk RNA sequencing revealed correlations between specific bacteria and cytokine dysregulation, and bacterial metagenomics predicted pathways associated with inflammation⁵⁸. Such approaches have great potential to expand our understanding of SpA.

Conclusion

With an ever-evolving scientific landscape, emerging molecular techniques continue to elucidate important pathways in complex diseases such as SpA. Within SpA, many of the previously described techniques are offering new approaches towards diagnosis such as metabolites, miRNA, or immune cell profiling as well as identifying new therapeutic approaches such as epigenetic modifications. In addition to the development of new techniques are the expansion of analysis tools in handling data generated from the methodologies. Altogether, these approaches provide exciting pathways forward in the study of SpA.

Key Points:

• Recent advances in genetics, ‘omics technologies, and immune profiling methods have contributed to significant advances in the clinical and scientific understanding of spondyloarthritis.

• Genetic studies have shifted from GWAS to newer technologies such as whole genome sequencing and identification of epigenetic modifications.

• Single cell sequencing and time of flight cytometry allow identification of novel cell populations in tissues.

• Multi-omic analyses will further integrate large data sets and inform pathophysiologic mechanisms.

Synopsis:

New and emerging molecular techniques are expanding our understanding of the pathophysiology of spondyloarthritis (SpA). GWAS studies identified novel pathways in antigen processing and presentation as well as Th17 immunity associated with SpA. Immune cell profiling techniques have supported Th17 immune responses and are increasingly revealing intestinal mucosal immune cells as associated with disease. Emerging technologies in epigenetics, transcriptomics, microbiome, and proteomics/metabolomics are adding to these, refining disease pathways and potentially identifying biomarkers for diagnosis and treatment responses. In this review we describe many of the new molecular techniques that are being utilized to investigate SpA.