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Keyword: autoimmune disease

Dr. Betty Pei-tie Tsao and colleagues

Betty Pei-tie Tsao, Ph.D. (front, center), Richard M. Silver Endowed Chair for Inflammation Research at MUSC and senior author on the Nature Genetics article, with first author Jian Zhao, Ph.D. (to Dr. Tsao's left) and second author Yun Deng, M.D. (to Dr. Tsao's right).

Investigators at the Medical University of South Carolina (MUSC)  report pre-clinical research showing that a genetic variant encoded in neutrophil cytosolic factor 1 (NCF1)  is associated with increased risk for autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis, and Sjögren's syndrome, in the January 2017 issue of Nature Genetics. Data indicate that increased NCF1 protects against SLE while decreased NCF1 raises SLE risk and highlight the pathogenic role of reduced reactive oxygen species in autoimmune disease development.

Single-nucleotide polymorphisms (SNPs – pronounced 'snips') are the most common type of human genetic variation; each one represents a small difference in a nucleotide – the building blocks of our DNA. The Immunochip for fine-mapping is an important tool for conducting genome-wide association studies of the genetic components of disease. Researchers use the Immunochip to investigate DNA samples from people with a particular disease for linkage disequilibrium (LD) signals that illuminate associations between specific SNPs and the disease. Autoimmune diseases such as SLE are known to have a strong genetic component and, to date, dozens of SNPs associated with SLE have been identified and included on the Immunochip.

The Achilles heel is, of course, that the Immunochip cannot identify associations with SNPs that it does not include.

When MUSC researchers genotyped DNA samples from Chinese, European-American, and African-American SLE patients, they found a strong signal in the Chinese sample at the rs73366469 locus in the GTF2IRD1–GTF2I intergenic region at 7q11.23. This was puzzling because that locus was not consistent with SLE loci identified by other genome-wide association studies. Furthermore, the very strong signal in the Chinese sample appeared as a modest signal in the European-American sample and did not appear at all in the African-American sample.

"A true risk gene should be the same in all populations,” explained Betty Pei-tie Tsao, Ph.D., Richard M. Silver Endowed Chair for Inflammation Research at MUSC and senior author on the article. “And for such a strong signal, we wondered, 'why hasn't anyone else seen it?' We wanted to find out if what we were seeing was true and explain it." 

The team confirmed their finding using a different genotyping platform in an independent Asian sample provided by Nan Shen, M.D., Ph.D., professor of medicine and director of the Shanghai Institute of Rheumatology at Shanghai Jiao Tong University's School of Medicine. But, because rs73366469 did not show LD with any SNPs in the Immunochip, the researchers hypothesized that the SNP containing the true underlying risk factor was not included in it.

"We sort of came into the study from our Asian samples and then started looking for this signal in other populations,” said Tsao. “Every ethnic group has a different ancestral background and different LD patterns. We used the LD signal strength as a guide to find our way to the true risk gene – the particular variant that actually caused the increased risk for lupus."

Because the SNP they were looking for was most likely not included in the Immunochip, the team turned to the 1000 Genomes Project dataset, where they found two SNPs that were not only not on the Immunochip, but also produced stronger LD signals with rs73366469 in Asian patients than European or African patients. One of these two, rs117026326 located on intron 9 of GTF2I, showed a stronger association with SLE than either the original or the other locus from the 1000 Genomes Project.

As the researchers focused in on rs117026326, they saw that the NCF1 gene was nearby. This was important because NCF1, which encodes a subunit of NOX2, is thought to be related to SLE due to its role in activating the phagocytic complex NOX2.

Preclinical studies have shown that non-functional NOX2 exacerbates lupus in mice. Furthermore, NCF2, which encodes another subunit of NOX2, is associated with SLE risk in European Americans. The strong association of rs117026326 with SLE and the functional implications of nearby NCF1 took the team to their next hypothesis: that the rs117026326 SNP might tag causal variants of NCF1 that were not present in the 1000 Genomes Project database.

But unraveling this mystery was not going to be easy.

 "This is a very complex genomic region,” explained Tsao. “The NCF1 gene has two nearly identical twins – NCF1B and NCF1C – that are 98% the same. But they are non-functional pseudo-genes. This makes working in this region of the human genome very difficult. That's why the next-generation sequencing method that the 1000 Genomes Project has been doing doesn't pertain to this region."

The researchers believed that mapping techniques commonly used by the larger projects, while efficient, limited their ability to find unique sequences among all the copies and duplications in this region. So, they decided to set up their own, novel PCR assay.

"You can't easily sequence this region using the next-generation techniques,” said Tsao. “So, we had to do it the old-fashioned way, which was very time consuming and labor intensive. To genotype the region correctly, we used PCR to selectively amplify the NCF1 copies and conduct copy number variation tests. Then we only used samples with no copy number variation to examine the NCF1 variant. This method ensured that what we identified as an NCF1 variant was truly a variant."

Using this strategy, the team identified 67 SNPs, four of which had a strong association with rs117026326. After conducting a long series of multiple tests in samples from various ethnic populations, they gradually eliminated three of the four SNPs and determined that the one called p.Arg90His was the likely genetic variant causing SLE susceptibility across all populations.

In addition, p.Arg90His was associated with increased risk for other autoimmune diseases, including rheumatoid arthritis and Sjögren's syndrome. The team also found that having only one copy of NCF1 was associated with a higher SLE risk, but having three or more  NCF1 copies was associated with reduced SLE risk. Finally, while the underlying mechanism is unclear, the team found that having reduced NOX2-derived reactive oxygen species also raised the risk for these autoimmune diseases.

Tsao notes that perseverance was a critical component of this work. This work was started years ago when the team was at the University of California Los Angeles  and was completed after moving to MUSC.

"We just stuck with it as a labor of love. Our lead author, Jian Zhao, devoted several years of his life to this project,” explained Tsao.” At the time we started, we didn't know it was going to be so complex. We just wanted to explain what we were seeing. It turned out to be quite a chase and very interesting and rewarding to finally bring this project to this point."

This work also points out an important unmet need in the field of genetic mapping.

"We need a more efficient platform to screen complex genome regions for variants. For a lot of diseases we've identified some, but not all, of the variants. There may be more variants hiding in these complex regions," said Tsao. "You have to sort it out like a puzzle. Autoimmune diseases share certain risk factors but also have unique genetic variants that drive the molecular pathogenesis of the disease. Each time you find a variant, you get more puzzle pieces and you can start to understand more about that disease and other autoimmune diseases as well."

MUSC Health Rheumatologist Dr. Jim Oates

 

Summary: Medical University of South Carolina (MUSC) investigators report preclinical research showing that prognostic models for lupus nephritis that include novel biomarkers have significantly improved predictive power over models using only traditional markers, in the August 2016 issue of Arthritis & Rheumatology. Data reveal that chemokines, cytokines, and markers of cellular damage were most predictive of patients' therapeutic response. This is a critical first step to developing clinically meaningful, decision-support tools in lupus nephritis.

 

 

Caption: MUSC Health rheumatologist Jim C. Oates, M.D.

Results of preclinical studies by investigators at the Medical University of South Carolina (MUSC) reported in the August 2016 issue of Arthritis & Rheumatology demonstrate for the first time that including novel biomarkers in lupus nephritis (LN) prognostic models significantly increases their power to predict therapeutic efficacy. Identifying biomarker models with sufficient predictive power is a critical step toward developing clinical decision-making tools that can rapidly identify patients who require a change in therapy and potentially reduce onset of renal fibrosis during induction therapy.

Approximately half of all patients with systemic lupus erythematosus (SLE) develop LN, an immune complex-mediated glomerulonephritis. Lupus nephritis, in turn, leads to renal failure in up to 50% of patients within five years. American College of Rheumatology guidelines recommend changing LN treatment after six months of induction therapy if response to therapy is not achieved. However, 'response to therapy' is not clearly defined and renal damage can occur during the six-month induction period.

Currently, clinicians monitor response to treatment via blood pressure measurements, serum complement levels, anti-double-stranded DNA (anti-dsDNA) antibody levels, urinary sediment, urinary protein-to-creatinine ratios, and surrogates of renal function. Unfortunately, predicting disease progression is difficult using these traditional biomarkers due to their low sensitivity and high LN heterogeneity at presentation. Even when machine learning models are employed, traditional biomarkers are only 69% accurate in predicting a LN diagnosis among SLE patients. There is a need for individualized, decision-support tools that can better define 'therapeutic response' at the start of therapy and allow clinicians to tailor induction therapy to disease severity to prevent renal damage and unnecessary drug toxicity.

"We saw our colleagues' frustration in trying to come up with predictive models,” said Jim C. Oates, M.D., Associate Director of the MUSC Clinical and Translational Research Center, Associate Professor of Rheumatology, and senior author on the article. “The traditional markers we use in clinic today have quite limited predictive capacity. All lupus patients have varying degrees of kidney damage and levels of involvement of the different kidney structures. So, we wanted to account for this heterogeneity and the stages of disease progression. We wanted to include markers for pathways of inflammation as well as for damage."

The research team hypothesized that a targeted panel of urinary biomarkers reflecting initial resident and inflammatory cell activation (cytokines), signals for homing to the kidney (chemokines), activation of inflammatory cells (growth factors), and damage to resident cells, combined with artificial intelligence/machine learning modeling, might provide an early LN decision-support tool that could predict outcomes better than standard biomarkers alone. The team also chose to assess urine biomarkers rather than serum/plasma markers to increase the tool's sensitivity and specificity to signals of renal (rather than systemic) processes.

Urine samples from 140 patients with biopsy-proven LN who had not yet started induction therapy were analyzed for a panel of novel biomarkers using pre-mixed, commercially available kits. Univariate, receiver operating characteristic (ROC) curves were generated for each biomarker and compared to ROC area under the curve (AUC) values from machine learning models developed using random forest algorithms. Outcome models using novel biomarkers plus traditional clinical markers demonstrated greater AUC and significance compared to models developed with traditional markers alone ([AUC 0.79; P<0.001] vs. [AUC 0.61; P=0.05], respectively). The combined models also demonstrated greater power to correctly predict LN therapy outcomes (responder versus non-responder) than models using only traditional markers (76% vs. 27%, respectively [p<0.002]).

The team identified chemokines, cytokines, and markers of cellular damage as most predictive of LN therapy response. Race, anti-double-stranded DNA antibodies, and induction medication did not significantly contribute to the model.

"We were somewhat surprised by some of the analytes that were important in the model,” said Oates. "One traditional marker, protein-to-creatinine ratio, was the third most important, and a standard kidney function measure was the ninth. I was also surprised to see interluekin-8 so high. This is in keeping with recent publications highlighting the importance of neutrophils in the pathogenesis of lupus, however."

Including multiple mechanisms of disease pathogenesis and cellular damage likely provides a more effective diagnostic approach by better reflecting the multi-stage, heterogeneous nature of LN. This is the first study to combine a broad biomarker panel with machine learning techniques to optimize disease outcome models. "This could apply to any model where there is kidney inflammation leading to damage,” said Oates. "It's proof of concept for other kidney diseases that you can take a discovery model and incorporate machine learning to develop and validate predictive models."

The team is now testing other biomarkers and applying the model in a larger patient population to ensure external validity and improve power. They are also exploring other inputs.

"Our next approach is to harness existing data in the medical record to enhance predictions,” said Oates. “This is much more immediately translatable in the clinic than getting through a long FDA validation process and the industry pipeline. Using medical record data is cheaper, and there are patient and system factors in the medical record that you can't measure with an assay, such as economic and societal disparities, which affect outcomes. This approach could also be used to enhance biomarker predictive models”

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