Pharmacogenomic research has been active in the area of hepatitis C virus infection. Both viral and host polymorphisms are discussed in this review to describe how the genotype information helps predict response to conventional peginterferon alfa and ribavirin therapy as well as more recently recommended direct-acting antivirals, simeprevir and sofosbuvir.

by M. Kawaguchi-Suzuki and Dr R. F. Frye

Hepatitis C virus infection
Hepatitis C virus (HCV) chronically infects 170 million people worldwide [1]. After exposure to HCV, some patients may experience fever, fatigue, dark urine, clay-coloured stool, abdominal pain, loss of appetite, nausea, vomiting, joint pain or jaundice [2]. However, the majority of patients are asymptomatic and do not seek medical attention, leading to the chronic infection rate of 75–85% [2]. HCV infection is a significant burden to society, not only because of the prevalence but also because the infection can lead to severe complications and mortality. Patients infected with HCV can develop chronic liver disease, progressing to advanced fibrosis and cirrhosis and eventually to hepatocellular carcinoma [1]. Additionally, HCV infection is the primary indication for liver transplantation in developed countries [1].

HCV is a blood-borne 9.6 kb positive-sense, single-stranded RNA virus [1, 2]. Typically, the first screening method used to diagnose HCV is the detection of anti-HCV antibodies [3]. Definitive diagnosis of HCV infection is then made by measurements of HCV RNA or ‘viral load’, with a sensitive molecular method having a lower limit of detection <15 IU/mL [3, 4]. Once a decision to treat has been made, the therapeutic goal is to achieve virologic cure or sustained virologic response (SVR) defined as undetectable HCV RNA 12 weeks after the completion of therapy [4]. Recent advancements in HCV treatment now make HCV curable for more patients, which will reduce mortality and liver-related health adverse consequences. Therapy for HCV infection
Since the identification of HCV in 1989, various treatment regimens have been used to treat HCV infection [1]. Interferon therapy was the first treatment against HCV and then combination therapy with ribavirin (RBV) became available in 1998 [1]. After the introduction of the pegylated formulation of interferon or peginterferon alfa (PEG) in 2001, the dual therapy of PEG and RBV has been the standard care for a decade, until the recent approval of direct-acting antivirals (DAAs) [1]. The first-generation DAAs are the protease inhibitors boceprevir and telaprevir. Subsequently, another protease inhibitor, simeprevir, and a polymerase inhibitor, sofosbuvir, were introduced. The three protease inhibitors were approved in combination with PEG and RBV. However, PEG-free regimens became an option for select patients with the approval of sofosbuvir.
Although the likelihood of achieving SVR has improved with each advance in treatment, HCV infection is a therapeutic area in which large inter-patient variability in response has been observed. In order to predict treatment response and to tailor therapy for individual patients infected with HCV, the extent to which pharmacogenomic biomarkers explain this variability has been examined.

Pharmacogenomics: viral polymorphism
HCV is categorized into genotypes 1–7 and further classified into subtypes a, b, etc., based on sequence divergence [1]. The observed HCV genotype depends largely on the geographic location; genotype 1 accounts for 70% of cases in the Americas, 50–70% of cases in Europe, and 75% of cases in Japan, with genotypes 2 and 3 being the next most prevalent [1]. In contrast, genotypes 3 and 6 have been widely identified in South and Southeast Asia, while genotypes 4 and 5 are most commonly observed in Africa [1]. Genotype 7 was most recently discovered, but its clinical importance has not yet been determined [1]. Identification of the HCV genotype is important for HCV-infected patients because treatment choice, therapy duration, and treatment response depend on the viral genotype. The first-generation DAAs and simeprevir are only indicated for genotype 1 infection. However, sofosbuvir has pan-genotypic activity and is approved for the treatment of genotypes 1, 2, 3, and 4 [5].

Before the approval of the use of DAAs, HCV therapy targeted the host immune system with the use of PEG. However, since therapy now directly targets the virus itself, various viral polymorphisms that may confer treatment resistance have been reported. The most notable one is the Q80K polymorphism observed in HCV genotype 1a [6]. Findings from the phase 3 QUEST-1, QUEST-2, and PROMISE trials are shown in Figure 1 [6]. QUEST-1 and QUEST-2 were conducted among treatment-naïve patients, whereas PROMISE was a study in patients who relapsed after previous treatment with an interferon-based regimen.

In general, HCV genotype 1a was considered less susceptible to the treatment, but when the data were analysed based on Q80K polymorphism, the SVR rates in genotype 1a were similar to those in genotype 1b if the Q80K polymorphism was absent. However, the SVR rates turned out to be even lower when the polymorphism was present. Based on these data, both prescribing information and clinical guidelines suggest alternative therapy if the Q80K polymorphism is detected in HCV genotype 1a infection [6]. Consequently, Q80K polymorphism testing is recommended by the clinical guidelines in all patients before the initiation of simeprevir, PEG, and RBV triple regimen [4].

Pharmacogenomics: host polymorphism
Earlier, various genome-wide association studies and candidate gene studies found two single nucleotide polymorphisms (SNPs) in the host were associated with SVR in HCV genotype 1 infection after PEG and RBV therapy [7]. The two SNPs, rs12979860 and rs8099917, are located in IFNL3 gene (previously called IL28B) [7]. Rs12979860 CC and rs8099917 TT genotypes have been considered as favourable response genotypes, with SVR rates of around 80% achieved in genotype 1 infection, whereas rs12979860 minor T allele or rs8099917 minor G allele carriers had lower SVR rates of about 20% [8]. SVR rates tend to be higher in HCV genotype 2 and 3 infections with PEG and RBV therapy, compared to those in genotype 1 therapy, and the association of these two SNPs with SVR has not been as strong in genotype 2 and 3 infections [7]. However, similar association of the two IFNL3 SNPs with SVR to that in genotype 1 infection has been shown in genotype 4 infection [7]. Although data has been scarce in other rare genotype infections, the IFNL3 genotype has been one of the strongest predictors of SVR with PEG and RBV therapy [7, 8]. The exact mechanism by which the IFNL3 SNPs affect the phenotype has not been fully elucidated, but baseline differences in the expression level of interferon-stimulated genes have been proposed [9]. Data has been collected with both IFNL3 SNPs, but rs12979860 is more commonly used if a single SNP has to be chosen for research or clinical purpose.

The association of treatment response with the IFNL3 genotype has also been observed with interferon-based DAA therapies. Currently, treatment with boceprevir or telaprevir is not recommended by the guidelines, and most commonly used regiments include simeprevir and/or sofosbuvir [4]. Figure 2 describes SVR rates in treatment-naïve and treatment-experienced patients who were treated with simeprevir or sofosbuvir combined with PEG and RBV as indicated in the prescribing information [5, 6]. Higher SVR rates were consistently observed in patients with the rs12979860 CC genotype, compared to the T allele carriers. However, it should be noted that the difference in SVR rates between the IFNL3 genotypes was comparatively modest with the addition of a DAA to the conventional PEG and RBV therapy. In addition, no significant difference was observed in SVR rates based on the IFNL3 genotype in patients infected with HCV genotype 2 or 3 after an interferon-free regimen of sofosbuvir and RBV [10].

Summary and future directions
Large inter-patient variability exists in response to PEG and RBV therapy, and the IFNL3 genotype has been demonstrated as one of the strongest predictors for SVR, especially in HCV genotype 1 infection. This trend has also been observed in interferon-based DAA therapies. However, if an interferon-free regimen becomes more readily available in the future, with the potential for SVR rates to approach 100%, the IFNL3 genotype may no longer hold clinical utility. However, IFNL3 genotype may still need to be tested during drug development to ensure that investigational agents will have efficacy in patients carrying the variant allele previously associated with an unfavourable treatment response. Additionally, when PEG is eliminated from treatment regimens and only DAAs are combined, cross-resistance may become a concern in the future, especially in patients who failed a DAA regimen previously. Various viral polymorphisms have been detected in protease inhibitors, and cross-resistance can be an issue in this drug class [6, 11, 12]. Viral polymorphisms may play a bigger role and need to be monitored for the future.

References
1. Scheel TK, Rice CM. Understanding the hepatitis C virus life cycle paves the way for highly effective therapies. Nat Med. 2013; 19(7): 837–849.
2. Centers for Disease Control and Prevention. Hepatitis C information for health professionals. 2013; http://www.cdc.gov/hepatitis/hcv/. Accessed January 11, 2014.
3. EASL. EASL Clinical Practice Guidelines: Management of hepatitis C virus infection. J Hepatol. 2014; 60(2): 392–420.
4. AASLD, IDSA, IAS–USA. Recommendations for testing, managing, and treating hepatitis C. 2014; http://www.hcvguidelines.org/.
5. Sovaldi (sofosbuvir) package insert. Gilead Sciences, Inc. 2013.
6. Olysio (simeprevir) package insert. Janssen Therapeutics. 2013.
7. Kawaguchi-Suzuki M, Frye RF. The role of pharmacogenetics in the treatment of chronic hepatitis C infection. Pharmacotherapy. 2014; 34(2): 185–201.
8. Pacanowski M, et al. New genetic discoveries and treatment for hepatitis C. JAMA. 2012; 307(18): 1921–1922.
9. Cariani E, et al. Translating pharmacogenetics into clinical practice: interleukin (IL)28B and inosine triphosphatase (ITPA) polymophisms in hepatitis C virus (HCV) infection. Clin Chem Lab Med. 2011; 49(8): 1247–1256.
10. Jacobson IM, et al. Sofosbuvir for hepatitis C genotype 2 or 3 in patients without treatment options. N Engl J Med. 2013; 368(20): 1867–1877.
11. Victrelis (boceprevir) package insert. Merck & Co., Inc. 2013.
12. Incivek (telaprevir) package insert. Vertex Pharmaceuticals 2013.

The authors
Marina Kawaguchi-Suzuki PharmD, BCPS; Reginald F. Frye* PharmD, PhD, FCCP
Department of Pharmacotherapy and Translational Research,
University of Florida College of Pharmacy, Gainesville,
FL 32610-0486, USA
*Corresponding author
E-mail: frye@cop.ufl.edu

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with diverse clinical manifestations. Accurate diagnosis, prediction of disease activity, organ involvement and management remains problematic owing to a lack of reliable biomarkers. This article reviews traditional and a few promising candidate biomarkers in SLE with specific clinical implications.

by Dr Anne E. Tebo

Background
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by heterogeneity in disease manifestations as well as diversity in immunologic and therapeutic responses. Despite longstanding research efforts, precise diagnosis and prediction of response to treatment remain problematic owing to variable disease presentation and course as well as a lack of sensitive and specific biomarkers [1, 2]. Several factors contribute to the immune abnormality and clinical heterogeneity that occurs in SLE; these include genetic, epigenetic, environmental, and hormonal influences. The interplay between these elements drives the production of a variety of autoantibodies, complement products, inflammatory markers and other mediators identified as biomarkers to diagnose, monitor, stratify and/or predict disease risk, course or response to treatment [1–7]. These biomarkers – genetic, biologic, biochemical or molecular – may correlate with disease pathogenesis or specific clinical manifestations and can be evaluated qualitatively or quantitatively in laboratories. Notable amongst these are diverse autoantibodies and complement products which (in addition to clinical manifestations such as skin lesions, arthritis, renal disorder, hematologic changes, neurologic disorder amongst others) are traditionally considered hallmarks of disease [8, 9]. This review article highlights traditional and a few promising candidate serologic, cellular and urine biomarkers for diagnosing and predicting disease activity as well as renal involvement in SLE.

Biomarkers for the diagnosis of SLE
The updated American College of Rheumatology (ACR) revised criteria for the classification of SLE [8] is largely used in clinical practice to diagnose patients. In addition to specific clinical manifestations, the guidelines recommend testing for antinuclear antibodies (ANA), anti-double stranded deoxyribonucleic acid (anti-dsDNA), anti-Smith (anti-Sm) and antiphospholipid antibodies (lupus anticoagulant, IgG and IgM antibodies to cardiolipin (aCL) and beta2 glycoprotein I (anti-β2GPI). Recently, the Systemic Lupus Collaborating Clinics proposed the SLICC criteria for SLE in view of recent knowledge of the immunology of SLE [9]. Based on the SLICC rule for the classification of SLE, a patient must satisfy at least four criteria, including at least one clinical criterion and one immunologic criterion or must have biopsy-proven lupus nephritis (LN) in the presence of ANAs or anti-dsDNA antibodies. SLE is also associated with a variety of extractable nuclear antibodies such as anti-SSA, anti-SSB, and anti-snRNP as well as anti-ribosomal P, anti-histone, anti-nucleosome, anti-PCNA and anti-C1q autoantibodies [3, 5, 6]. The diagnostic characteristics of these autoantibodies (especially the anti-dsDNA antibodies) have been reported to be variable, which may be attributable the diversity of analytical methods, target antigens, patient demographics and SLE clinical subsets investigated [3, 5, 6, 11].

In addition to specific autoantibody tests, the proposed SLICC criteria recommend testing for complements C3, C4 and CH50 as well as using the direct Coombs assay. In the past several years, serum C3, C4 and CH50 levels have traditionally been used to diagnose and monitor disease activity in SLE patients [reviewed in 1, 2, 4]. However, in vitro activation may compromise interpretation of results and serum complement levels do not differentiate between consumption and production, which may be important for diagnosis. There is some evidence that cell-bound complement activation products (CB-CAPs) may facilitate SLE diagnosis [4, 12, 13]. These include complement C4-derived ligand deposited on erythrocytes (EC4d), platelets (PC4d), B lymphocytes (BC4d) and reticulocytes as detected by flow cytometry. Compared to disease controls, there is a relative increase of cell-bound C4d (CB-C4d) in SLE. However, the actual relevance of a single CB-C4d assay to the diagnosis of SLE is thought to be unlikely, although a panel of EC4d and BC4d assays is proposed to be predictive of SLE [12]. Further studies in diverse SLE clinical subsets and populations are required to determine the optimal CB-C4d panels for diagnostic evaluation.

Biomarkers for assessing disease activity
There are currently no consensus measures or biomarkers to reliably evaluate disease activity, predict flares and their differentiation from permanent damage in SLE patients. Disease activity indices such as the SLE disease activity index 2000 (SLEDAI-2K), British Isles Lupus Assessment Group (BILAG 2004), and the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) are all complex and mostly used in academic centres and/or in clinical trials [reviewed in 14]. In addition to routine serologic markers for inflammation, anti-dsDNA antibodies, C3, and C4 have been traditionally used to assess disease activity and predict flares in SLE (Table 1). For patients with LN, urine analysis for protein, sediment, protein-creatinine ratio and albumin are used to evaluate activity, monitor treatment response and predict relapse. Several studies have examined the associations between traditional and non-traditional biomarkers to determine optimal panel of markers associated with disease activity or predicting severity [reviewed in 1–7]. The outcomes in these investigations have been inconsistent, probably due to variability in detection methods and heterogeneity in patient populations. These inconsistencies have hampered the adoption of an acceptable biomarker panel to evaluate and monitor disease activity.

A number of promising candidate biomarkers associated with disease activity in SLE have been identified and are reviewed in other publications [1, 2, 4–7]. These include biomarkers for serologic (autoantibodies, cytokines and cytokine receptors, markers of endothelial cell activation, and soluble cell surface molecules), cellular (cell-bound C4d, CD27high plasma cells and other lymphocyte subsets) and urine [neutrophil gelatinase-associated lipocalin (NGAL), sVCAM-1 (soluble vascular cell adhesion molecule 1), MCP-1 (monocyte chemotactic protein 1), and TWEAK (tumor necrosis factor-like weak inducer of apoptosis), immunoglobulin free light chain, von Willebrand factor, IL-6] analyses. Of the several autoantibodies described, anti-nucleosome and anti-C1q antibodies appear to significantly correlate with disease activity and/or predict flares [2, 4, 6]. Chromatin, the DNA-histone complex found in the nucleus is organized into a repeating series of nucleosomes. In SLE patients, anti-nucleosome antibodies are more likely to be detected in patients with LN and may serve as a useful biomarker in the diagnosis of active LN [reviewed in 2]. Anti-C1q IgG antibodies that target epitopes present within the collagen-like tail of C1q are also seen in varying prevalence in SLE patients. Increasing titres in anti-C1q antibodies have been suggested to predict renal flares [2, 4, 6]. Other serologic biomarkers such as cytokines (IFN-α/β, IFN-γ, IL-1, IL-6, IL-10, IL-12, IL-15, IL-17, IL-21, and TNF-α), IFN-inducible chemokines, a few cytokine receptors, complements, specific markers of endothelial cell activation, BLys and several others associated with SLE pathogenesis and disease activity have been described [1, 2, 4]. However, these have mostly been reported in research studies and are beyond the scope of this article.

Quite a number of studies have shown that elevated levels of complement activation cleavage products may reflect disease activity more accurately and are more likely than conventional measurements in the prediction disease flares. Of these, the best described is the Cd4 fragment, which is capable of binding several cell types. In one study, EC4d levels were observed to be higher in patients with ‘more active’ and ‘most active’ SLE compared with those with ‘less active’ disease [13]. EC4d measurements were also found to be associated with specific measures of disease activity even after adjusting for serum levels of C3, C4 and anti-dsDNA antibodies [13]. However, independent prospective investigations with appropriate controls are needed to validate these observations.

Biomarkers for renal involvement
Of the different clinical subsets of SLE, LN is one of the most common and associated with significant morbidity and mortality. In the US, approximately 35% of adults with SLE have clinical evidence of nephritis at the time of diagnosis, with an estimated 50–60% developing nephritis during the first 10 years of disease [reviewed in 15]. Among these patients, quite a few will progress to end-stage renal disease. Improved methods for detecting LN would allow earlier treatment preventing irreversible impairment of renal function and damage. In place of invasive, subjective and costly serial renal biopsies, tests such as creatinine clearance, levels of urine protein and sediment as well as serologic determinations of C3, C4, creatinine level and anti-dsDNA titres have for decades been used to follow the onset, course, and severity of LN. It is however, recognized that these analyses are inadequate as they are limited in responsiveness to change and therefore unsuitable for patient care [7, 15].

In an effort to reliably diagnose LN, several candidate biomarkers have identified [1, 2, 7]. Among autoantibodies, antichromatin/anti-nucleosome and anti-C1q antibodies have shown some promise as biomarkers of renal involvement as previously described in this article. Of the urine protein biomarkers NGAL, sVCAM-1, MCP-1 and TWEAK, amongst others, have received considerable attention (Table 1) [1, 2, 7]. Of these, NGAL has been much studied. It is a small protein expressed in the neutrophils and certain epithelial cells, including the renal tubules. Under normal physiologic conditions, NGAL expressions are low in urine and plasma, but quickly rise from basal concentrations in response to kidney injury to reach diagnostic thresholds within a very short period of time. This is in contrast to the routinely used kidney function tests such as creatinine, where increased concentrations may not be observed until 24 to 48 hours after injury and often lack sensitivity. Urine NGAL is, however, not specific for SLE and further studies are necessary to establish accurate reference ranges based on age, gender and ethnicity. Like NGAL, urine sVCAM-1, sICAM-1 (soluble intercellular CAM-1) and MCP-1 have been shown in human studies to be strongly correlated with LN activity and severity [reviewed in 7]. With the identification of these novel urinary biomarkers for diagnosing and differentiating active versus inactive LN, several studies have examined their relationship with histological features of LN. Brunner et al. 2012 examined a number of established markers (anti-dsDNA, serum C3, C4, creatinine, urinary protein : creatinine ratio, etc.) and a few candidate urinary biomarkers [MCP-1, NGAL, lipocalin-type prostaglandin D-synthetase (L-PGDS), α1-acid-blycoprotein (AAG/AGP), transferrin (TF), and ceruloplasmin (CP)] in urine samples from 76 SLE patients collected within 2 months of kidney biopsy [reviewed in 7]. These urinary biomarkers were compared with histopathologic features of the kidney biopsy such mesangial expansion, capillary proliferation, crescent formation, wire loops, or fibrosis. Overall, their results indicated that levels of specific urinary biomarkers were increased in active LN and appeared to correlate with distinctive histologic features in renal biopsies. Furthermore, based on the presence of defined urine proteins, the authors could predict specific LN signatures. LN activity signature was defined by a combination of urinary MCP-1, AAG, and CP levels and protein : creatinine ratio while LN chronicity was characterized by NGAL, MCP-1 and creatinine clearance. The combined tests of MCP-1, AAG, TF, creatinine clearance and serum C4 was indicative of a potential biomarker panel for membranous nephritis. However, like NGAL, increased expression of these urine biomarkers is not exclusive to LN. Future studies are likely to highlight the relevance of specific urine biomarker proteins in predicting renal involvement in SLE.

Conclusion
There is considerable evidence that no single biomarker will be sufficient to diagnose, monitor and stratify all patients with SLE. Ideally, new biomarkers should provide information not available from traditional tests. Recent efforts geared towards the discovery and validation of biomarker ‘panels’ or ‘signatures’ of SLE represent an informed approach. Validation studies with endpoints that ensure a true measure of the intended clinical process in diverse cohorts coupled with robust analytical assays and high statistical power to confirm these panels are needed.

References
1. Illei GG, Tackey E, Lapteva L, Lipsky PE. Biomarkers in systemic lupus erythematosus. I. General overview of biomarkers and their applicability. Arthritis Rheum. 2004; 50: 1709–1720.
2. Liu CC, Kao AH, Manzi S, Ahearn JM. Biomarkers in systemic lupus erythematosus: challenges and prospects for the future. Ther Adv Musculoskelet Dis. 2013; 5: 210–233.
3. To CH, Petri M. Is antibody clustering predictive of clinical subsets and damage in systemic lupus erythematosus? Arthritis Rheum. 2005; 52: 4003–4010.
4. Leffler J, Bengtsson AA, Blom AM. The complement system in systemic lupus erythematosus: an update. Ann Rheum Dis. 2014; 73: 1601–1606.
5. Jeltsch-David H, Muller S. Neuropsychiatric systemic lupus erythematosus: pathogenesis and biomarkers. Nat Rev Neurol. 2014; 10(10): 579–596.
6. Cozzani E, Drosera M, Gasparini G, Parodi A. Serology of lupus erythematosus: correlation between immunopathological features and clinical aspects. Autoimmune Dis. 2014; 2014: 321359.
7. Bennett M, Brunner HI. Biomarkers and updates on pediatrics lupus nephritis. Rheum Dis Clin North Am. 2013; 39: 833–853.
8. Hochberg MC. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997; 40: 1725.
9. Petri M, Orbai AM, Alarcón GS, et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012; 64: 2677–2686.
10. Yu C, Gershwin ME, Chang C. Diagnostic criteria for systemic lupus erythematosus: a critical review. J Autoimmun. 2014; 48–49: 10–13.
11. Isenberg DA, Manson JJ, Ehrenstein MR, Rahman A. Fifty years of anti-ds DNA antibodies: are we approaching journey’s end? Rheumatology (Oxford) 2007; 46: 1052–1056.
12. Kalunian KC, Chatham WW, Massarotti EM, Reyes-Thomas J, Harris C, Furie RA, Chitkara P, Putterman C, Gross RL, Somers EC, Kirou KA, Ramsey-Goldman R, Hsieh C, Buyon JP, Dervieux T, Weinstein A. Measurement of cell-bound complement activation products enhances diagnostic performance in systemic lupus erythematosus. Arthritis Rheum. 2012; 64: 4040–4047.
13. Kao AH, Navratil JS, Ruffing MJ, Liu CC, Hawkins D, McKinnon KM, Danchenko N, Ahearn JM, Manzi S. Erythrocyte C3d and C4d for monitoring disease activity in systemic lupus erythematosus. Arthritis Rheum. 2010; 62: 837–844.
14. Romero-Diaz J, Isenberg D, Ramsey-Goldman R. Measures of adult systemic lupus erythematosus: updated version of British Isles Lupus Assessment Group (BILAG 2004), European Consensus Lupus Activity Measurements (ECLAM), Systemic Lupus Activity Measure, Revised (SLAM-R), Systemic Lupus Activity Questionnaire for Population Studies (SLAQ), Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K), and Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI). Arthritis Care Res. (Hoboken). 2011; 63(Suppl 11): S37–46.
15. Hahn BH, McMahon MA. American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis. Arthritis Care Res. (Hoboken). 2012; 64: 797–808.

The author
Anne E. Tebo^1,2 PhD
¹Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
²ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT 84108, USA
E-mail: anne.tebo@hsc.utah.edu;
anne.tebo@aruplab.com

Clostridium difficile causes serious life-threatening infections but this organism has a complex pathogenesis that makes differentiating true infection from asymptomatic carriage difficult. There are a number of diagnostic testing approaches that can be used alone or in multi-step algorithms. This review discusses the impact that the type of diagnostic test has on interpretation of clinically significant infection, initiation of treatment of C. difficile infection, and how future diagnostic testing may need to differentiate asymptomatic carriage from clinically significant disease.

by Dr Michelle J. Alfa

Clostridium difficile pathogenesis
Since the initial report by Bartlett et al. in 1978 [1] that C. difficile could cause infectious diarrhoea in patients who were treated with antibiotics (in particular clindamycin), there have been significant changes in the understanding of the pathogenesis of this organism as well as the approach to the diagnosis of the illness it causes. Initially, C. difficile was thought to be solely a hospital-acquired infection associated with a history of antibiotic consumption. Subsequently it became clear that humans can have toxigenic C. difficile present asymptomatically in their gastrointestinal tract and, unlike other enteric pathogens, the concept of ‘infectious dose’ does not really apply to this gastrointestinal pathogen. C. difficile infection (CDI) is a ‘two-hit’ process (Fig. 1). The first hit is ingestion of the metabolically inactive spore form which does not produce toxin, and the second hit is an imbalance of the gut microbiome (most often due to antibiotic therapy that eradicates gut normal flora without killing the spore or vegetative form of C. difficile). These two hits allow the ingested spore to germinate in the gut to the vegetative form which then replicates and produces Toxin A (enterotoxin) and Toxin B (cytotoxin). These toxins work synergistically to cause mucosal inflammation in the colon and diarrhoea (the small intestine is not damaged). Although the toxins do not appear to spread systemically, there is evidence that humoral antibodies against C. difficile Toxin A and B are protective.

In addition to exposure to spores in the healthcare environment or on the hands of caregivers, recent evidence implicates food products (beef, pork, fowl) as a source of C. difficile spores. This food reservoir may be the basis of community-acquired CDI (CA-CDI) as it is now recognized that up to 30% of all CDIs are acquired outside of healthcare facilities and, as discussed by Humphries et al. [2], patients with CA-CDI are more likely to have mild disease, shorter hospital stay and lower rates of mortality. Unlike other enteric vegetative bacterial pathogens in food products that are killed by adequate cooking, the spore form of C. difficile is not killed by cooking [3]. Consumption of C. difficile spores via food or iatrogenic exposure does not automatically lead to disease. Indeed up to 10–20% of healthy people and up to 70% of healthy neonates may harbour this toxigenic C. difficile in their gut but be asymptomatic [3, 4]. It is unknown if this represents transient passage of the ingested spores in the gut where the microbiome keeps C. difficile spores from germinating and replicating thereby preventing toxin production, or whether there can be asymptomatic colonization by toxigenic C. difficile at such low levels that there is no mucosal damage or diarrhoea. Guerrero et al. [5] reported that 12% of asymptomatic patients screened carried toxigenic C. difficile. Although the skin levels and environmental shedding from asymptomatic carriers was lower than from patients with CDI, it has been suggested [5] that asymptomatic carriers may still represent a significant reservoir for transmission within healthcare facilities.

The unique characteristics of C. difficile that include spore formation, asymptomatic carriage and ‘two-hit’ pathogenesis present challenges in terms of optimizing and interpreting diagnostic tests.

Diagnostic testing for toxigenic C. difficile
Over the past 20 years there has been a dramatic revolution in the approach to diagnostic testing for toxigenic C. difficile. Initially in the 1970s the diagnosis of C. difficile-associated diarrhoea was made by culture and subsequent testing of C. difficile isolates to determine if they were toxigenic or not [4, 6]. This was replaced by the cytopathic effect (CPE) assay in the late 1970s and early 1980s that detected biologically active Toxin B directly from the stool sample. Some still consider the CPE assay to be the most clinically relevant diagnostic test as it demonstrates there is sufficient biologically active toxin in stool to cause mucosal damage and diarrhoea. Because culture and CPE assays were labour intensive, costly, time consuming and required specialized expertise, antigen detection assays became the diagnostic test of choice early in the 1990s [4]. However, recent studies have documented that enzyme immunoassay (EIA) for Toxin A and B alone is insensitive and should not be used as a sole diagnostic test for CDI [2, 4, 6–10]. Some researchers advocate that toxigenic culture is the most sensitive diagnostic test [9]. Isolates must subsequently be tested to confirm they are toxigenic. Because toxigenic culture is too slow for clinical testing, multi-step algorithms using glutamate dehydrogenase (GD) antigen as a screen followed by CPE or nucleic acid amplification tests (NAATs) (Table 1) have been recommended [4, 6]. Within the past 5 years there has been a push towards using NAAT alone as the most rapid and sensitive diagnostic test for toxigenic C. difficile [4, 6, 8].

Longtin et al. [7] have recommended that diagnostic testing for C. difficile should be standardized because reportable rates of CDI are dramatically affected by the diagnostic test method or test algorithm utilized. They undertook a one year prospective study and reported that using NAAT alone instead of a multi-step algorithm based on GD antigen, Toxin A/B antigen and CPE assay resulted in a greater than 50% increase in CDI rate in their facility (8.9 cases by NAAT versus 5.8 cases by multi-step algorithm per 10,000 patient days). Their study was the first to report that for patients who were test positive by NAAT alone there was a 3% complication rate compared to the 39% complication rate for patients who were positive by both NAAT and their multi-step algorithm. The lack of standardization in diagnostic testing means the incidence rates reported will vary depending on the test method(s) used. The resultant increase in CDI incidence using NAAT tests compared to other testing algorithms has implications including; Medicare reimbursement penalties in the USA, financial penalties for increased CDI rates in England, target rates in Quebec, Canada.

Additional research is needed to clarify the clinical significance of NAAT positive tests when CPE and antigen tests are negative. As suggested by a variety of published reports [6–8, 10, 11], it may be wrong to assume that higher sensitivity makes for a better C. difficile diagnostic test. Leslie et al. [12] reported that quantitation of C. difficile copy number is reliable and they suggested this added information may help determine when therapy is warranted for NAAT positive tests. They reported that 30.6% of stools that were only positive by NAAT, and had no toxin detected by CPE or antigen testing had low C. difficile copy number/ml. Their data suggests that a large portion of NAAT positive samples fall into this category of ‘questionable’ clinical significance. Vancomycin treatment of asymptomatic C. difficile carriers has been shown to itself stimulate CDI and indeed the authors warned against antibiotic treatment for asymptomatic C. difficile carriers. It may be that detection by NAAT of low organism load represents spores (i.e. no toxin present) or may represent vegetative levels that do not require antibiotic therapy. Dionne et al. [10] reported good correlation between low levels of viable C. difficile and test positivity by NAAT only (i.e. negative by CPE and antigen detection). Furthermore, they were able to demonstrate a correlation between PCR cycle time (CT) and the level of viable C. difficile in the stool sample (Table 2).

Although many published manuscripts and reviews list sensitivity and specificity values, these are all dependent upon what is used as the ‘gold standard’. For C. difficile this is not a simplistic issue. It is clear that detection of toxigenic C. difficile by culture does not always indicate clinically significant disease.

Conclusions
As summarized in Table 3, there are a number of unresolved issues relating to diagnostic testing for C. difficile. For asymptomatic carriers of C. difficile who do not have diarrhoea, the concept of NAAT admission screening and contact precautions for those who test positive has yet to be determined to be beneficial in preventing the spread of CDI. For those patients with diarrhoea it is apparent that CDI rates will vary dramatically depending on the testing algorithm used. It is clear that NAAT used alone as a sole diagnostic test will overestimate CDI rates and could lead to unnecessary antibiotic therapy. The impact is substantive as about one-third of all NAAT positive results fall into this ‘grey area’ of doubtful clinical relevance (i.e. have no detectable toxin in the stool sample and/or are toxigenic culture negative).

In conclusion, a combination NAAT test that provides a quantitative assessment of the load of C. difficile in stool along with detection of C. difficile toxin genes appears to be the ideal combination of data in order to reliably determine which patients have clinically significant CDI and require treatment. However, prospective studies that assess clinical outcome based on this quantitative NAAT testing are needed to confirm that this diagnostic approach is optimal.

References
1. Bartlett JG, Chang TW, Gurwith M, Gorbach SL, Onderdonk AB. Antibiotic-associated pseudomembranous colitis due to toxin-producing clostridia. N Engl J Med. 1978; 298(10): 531–534.
2. Humphries RM, Uslan DZ, Rubin Z. Performance of Clostridium difficile toxin enzyme immunoassay and nucleic acid amplification tests stratified by patient disease severity. J Clin Microbiol. 2013; 51(3): 869–873.
3. Gupta A, Khanna S. Community-acquired infection: an increasing public health threat. Infect Drug Resist. 2014; 7: 63–72.
4. Tenover FC, Baron EJ, Peterson LR, Persing DH. Laboratory diagnosis of Clostridium difficile infection can molecular amplification methods move us out of uncertainty? J Mol Diagn. 2011; 13(6): 573–582.
5. Guerrero DM, Becker JC, Eckstein EC, Kundrapu S, Deshpande A, Sethi AK, et al. Asymptomatic carriage of toxigenic Clostridium difficile by hospitalized patients. J Hosp Infect. 2013; 85(2): 155–158.
6. Dubberke ER, Han Z, Bobo L, Hink T, Lawrence B, Copper S, et al. Impact of clinical symptoms on interpretation of diagnostic assays for Clostridium difficile infections. J Clin Microbiol. 2011; 49(8): 2887–2893.
7. Longtin Y, Trottier S, Brochu G, Paquet-Bolduc B, Garenc C, Loungnarath V, et al. Impact of the type of diagnostic assay on Clostridium difficile infection and complication rates in a mandatory reporting program. Clin Infect Dis. 2013; 56(1): 67–73.
8. Brecher SM, Novak-Weekley SM, Nagy E. Laboratory diagnosis of Clostridium difficile infections: there is light at the end of the colon. Clin Infect Dis. 2013; 57(8): 1175–1181.
9. Cohen SH, Gerding DN, Johnson S, Kelly CP, Loo VG, McDonald LC, et al. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the society for healthcare epidemiology of America (SHEA) and the infectious diseases society of America (IDSA). Infect Control Hosp Epidemiol. 2010; 31(5): 431–455.
10. Dionne LL, Raymond F, Corbeil J, Longtin J, Gervais P, Longtin Y. Correlation between Clostridium difficile bacterial load, commercial real-time PCR cycle thresholds, and results of diagnostic tests based on enzyme immunoassay and cell culture cytotoxicity assay. J Clin Microbiol. 2013; 51(11): 3624–3630.
11. Su WY, Mercer J, Van Hal SJ, Maley M. Clostridium difficile testing: have we got it right? J Clin Microbiol. 2013; 51(1): 377–378.
12. Leslie JL, Cohen SH, Solnick JV, Polage CR. Role of fecal Clostridium difficile load in discrepancies between toxin tests and PCR: is quantitation the next step in C. difficile testing? Eur J Clin Microbiol Infect Dis. 2012; 31(12): 3295–3299.

The author
Michelle J. Alfa, PhD
Boniface Research Centre, Dept. of Medical Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada
E-mail: malfa@dsmanitboa.ca

Kidney disease is one of the most life-threatening complications of diabetes and as the global incidence of diabetes soars, largely due to the dramatic increase in type 2 diabetes (T2DM), there will be a seismic shift in the number of patients in need of treatment through dialysis or transplant. Since up to 40% of diabetic patients develop symptoms of diabetic kidney disease (DKD), accurate and early identification of which patients are at the highest risk of progression from DKD to end stage renal disease (ESRD) will enable early initiation of protective renal therapies with subsequent reduction in healthcare costs and improved patient outcomes.

The cytokine TNFα, part of the Tumour Necrosis Factor (TNF) superfamily that plays a key role in homeostasis, has been implicated in the pathogenesis of diabetic kidney disease for over 20 years [1]. Researchers conclude that the elevated levels seen in diabetic patients could be the result of a TNFα driven dysregulation of the inflammatory/apoptotic pathways, which leads to kidney injury. The spotlight has recently shifted onto the TNF α receptors, Tumour Necrosis Factor Receptor 1 (TNFR1) and Tumour Necrosis Factor Receptor 2 (TNFR2), after a number of studies showed how elevated levels of these proteins were a predictor of progressive kidney disease.

In this article we look at the development of an In-Vitro Diagnostic test (IVD), the ‘EKF sTNFR1 Test’. This has been developed by EKF Diagnostics to measure levels of TNFR1 in plasma or serum in light of scientific evidence that this robust biomarker provides valuable prognostic information for diabetic patients at risk of progressive renal decline and ESRD.

The scientific evidence for the involvement of TNF receptors in kidney disease
Cytokine TNFα is a transmembrane protein generated by many cells, including lipocytes, endothelial cells and leukocytes. After processing by TNFα-converting enzyme (TACE), the soluble form of TNFα is cleaved from transmembrane TNFα and mediates its biological activities through binding the receptors TNFR1 and TNFR2 either in their transmembrane or soluble forms to activate inflammatory and stress response pathways (Figure 1). Transmembrane TNF-α also binds to TNFR1 and TNFR2 so that both transmembrane and soluble TNF-α can mediate downstream signalling events (apoptosis, cell proliferation and cytokine production).

In 2009, at the Joslin Diabetes Center, USA (the world’s largest diabetes research centre and an affiliate of the Harvard Medical School), researchers found that the presence of circulating soluble TNF receptors (sTNFR1 and sTNFR2) were strongly correlated with decreased renal function, or glomerular filtration rate (GFR). The research threw up questions about why these soluble receptors were indicative of renal disease. Were they playing an active part in causing disease, or were they just the by-product of the process? Elevations in circulating sTNFR1 have previously been reported in a wide variety of clinical conditions including cancer, congestive heart failure, rheumatoid arthritis, neurological diseases and infection; so what was their role in kidney disease?

Interestingly, as Niewczas et al. [2] pointed out, the decline of renal function was occurring in T1DM patients who had normal albumin excretion levels. This gave a clue to the researchers that the concentrations of these receptors were not merely markers of the injury leading to ESRD but were also involved in the inception of renal function decline, playing a part in inflammation and apoptosis.

1n 2012, the Joslin researchers published two further studies, on Type 1 and Type 2 diabetes cohorts, [3,4] and found that TNF receptor levels were robust predictors of progressive decline in GFR. The results showed that Type 1 Diabetes patients who had normal renal function at the onset, but TNFR2 levels in the highest quartile had a 60% cumulative incidence of reaching stage 3 Chronic Kidney Disease (CKD) with subsequent risk of progression to ESRD (compared to less than 20% in the lowest three quartiles) (Figure 2).

Most significantly, in Type 2 Diabetes patients with evidence of overt Kidney Disease (as evidence by elevated levels of albumin excretion levels) at the onset of the study, those with levels of TNFR1 in the fourth quartile had an 80% chance of developing renal disease over the twelve year period (compared to less than 20% of those in the lower three quartiles) (Figure 3).

These studies revealed that elevated TNF Receptor levels were a robust predictor of progressive disease in both Type 1 diabetes and Type 2 diabetes. In both studies, the levels of the TNFα levels also tended to predict progressive kidney disease, but less strongly than the TNF receptor levels. The data provided further evidence that inflammation in general, and the TNFα signalling pathway in particular, plays a role in kidney disease.

TNF receptors (TNFR1 and TNFR2) and their role in the disease process
So how are circulating TNFR receptors associated with early GFR reduction and kidney damage? It is known that the 55 kD TNFR1 and 75 kD TNFR2 receptors play a crucial part in apoptosis, survival and key aspects of the inflammatory and immune response. TNFR1 is abundant on all nucleated cells, but TNFR2 expression is restricted mainly to endothelial cells and leukocytes although this varies between normal and diseased tissues. Circulating TNFR1 in the plasma is released by two mechanisms: the inducible cleavage of the 34 kD TNFR1 extracellular domain by an enzyme known as ADAM17 and the constitutive release of a full-length 55 kD TNFR1 within exosome-like vesicles.

It is not-well understood whether the same mechanisms apply to TNFR2 release, or how this process is regulated and the biology of the soluble forms remain largely undiscovered. What is understood, however, is that in plasma, TNF receptors block TNFα from binding its target cell surface receptor and can therefore cause a prolonged and delayed effect of the cytokine. How subsequent damage occurs to the kidney is not well known, however sTNFRs have been shown to be involved in tubulointerstitial fibrosis, the characteristic tissue scaring that leads to kidney disease [5].

Seeing into the future: a powerful diagnostic test for DKD
The diagnosis of DKD is conventionally made by assessment of overall GFR and the presence of kidney damage is ascertained by either kidney biopsy or other markers of kidney damage such as microalbuminuria or proteinuria (collectively known as albuminuria – a condition where protein is lost in the urine).  GFR is estimated in clinical practice using readily calculated equations that adjust serum creatinine values (measurement of the by-product of muscle metabolism cleared by the kidneys) to age, sex, and ethnicity. However, while laboratory tests which assess both serum creatinine and albuminuria are inexpensive and readily available, these parameters have a low predictive value.

In 2012, EKF Diagnostics signed an exclusive licence agreement for novel kidney biomarker technology that focused on sTNFR1 and sTNFR2. This was developed by a team led by Prof. Andrzej Krolewski, MD, PhD, Head of Section on Genetics and Epidemiology at the Joslin Diabetes Center, Professor of Medicine at Harvard Medical School. Prof. Krowlewski was recently awarded the American Diabetes Association’s 2014 Kelly West Award in Epidemiology for services to diabetes epidemiology.

EKF Diagnostics has worked in partnership with Joslin and other key diabetes research centres to further validate the clinical utility of the markers and develop its first IVD product, the sTNFR1 test kit. The sTNFR1 test is an easy-to-use, microtitre plate ELISA-based assay requiring minimal training, which uses standard laboratory equipment and monoclonal antibodies to analyse just 50 µL of blood serum or plasma. Accurate and reliable results are obtained in a few hours and the standard assay format means that the test requires minimal training.

Julian Baines, Group Chief Executive Officer of EKF Diagnostics highlights the benefits of the test, “Our new sTNFR1 test adds greatly to information provided by standard clinical tests and provides valuable long term prognostic information for progressive renal decline to ESRD with the potential to streamline diabetic patient management, reduce time and costs and improve patient outcomes.”

Further evidence for the use of sTNFRs for the early prediction of DKD
A number of high impact studies published this year have independently corroborated the original research by the Joslin Diabetes Center. This newly published data from eminent European research centres in France (SURDIAGNE Study Group) and Finland (FinnDiane Study Group) add to the expanding data set underpinning the value of sTNFR1/2 biomarkers.

In the FinnDiane cohort study of over 400 subjects with Type 1 Diabetes followed over an average of 9 years, researchers found that, “Circulating levels of sTNFR1 were independently associated with incidence of ESRD. This association was reported as both significant and biologically plausible and demonstrated added value of sTNFR1 as a biomarker” [6].

In France, Saulnier et al. [7] found results from a study of n=522 Type 2 Diabetes patients with DKD were in accordance with published data, showing a deleterious effect of TNFR1 serum concentrations on renal outcomes.

Further evidence continues to mount for how TNFR biomarkers could be used to improve diabetic patient management and outcomes through early intervention.  Lopes-Virella et al. [8] have shown in a large cohort of type 1 diabetes patients, followed for six years, how high levels of sTNFR1 and sTNFR2 can predict progression to macroalbuminuria in patients completely free of disease at baseline. TNFR biomarkers can also help doctors to stratify patients with early kidney disease according to the risk of ESRD. Skupien et al [9] show a strong association between a single baseline measurement of TNFR2 serum concentration combined with measurement of HbA1c levels and the future rate of renal function decline in T1DM patients with proteinuria. Identifying patients at highest risk can ensure they are enrolled in therapeutic programmes to delay the rapid decline in renal function.

The future management of kidney disease
Recent statistics show that 25-40% of patients with diabetes are at significant risk of progression to ESRD and cardiovascular morbidity and mortality [10]. The global increase in the incidence in Type 2 diabetes will put more pressure on healthcare systems making it imperative to identify patients at risk of progressive diabetic kidney disease, and initiate protective renal and cardiovascular therapies. Improving outcomes for chronic kidney disease in diabetic patients also has an important impact on mortality; for example, compared with non-diabetic individuals, patients with Type 1 diabetes have no increase in mortality in absence of DKD [11]. There is now solid evidence that sTNFR1 and sTNFR2 can be useful as biomarkers to predict the progression of kidney disease – and not just in patients with diabetes:  recent research in Sweden has shown how circulating sTNFRs are relevant biomarkers for kidney damage and dysfunction in elderly individuals in a community setting [12].

Current treatments for CKD, such as control of hypertension and lifestyle interventions (weight loss, diet control, smoking cessation), can reduce the risk of progression to ESRD; therefore, an advanced knowledge of disease risk up to 10 years in advance that the sTNFR1 test kit can provide would be an extremely valuable tool to effectively prevent or reduce morbidity and mortality.  Significantly, the sTNFR1 test is also contributing to the development of new targeted therapies aimed at delaying or halting decline in renal function.

References
1.  Hasegawa G et al. Possible role of tumor necrosis factor and interleukin-1 in the development of diabetic nephropathy. Kidney Int. 1991; 40: 1007 –1012.
2. Niewczas MA et al. Serum concentrations of markers of TNF alpha and Fas-mediated pathways and renal function in nonproteinuric patients with type 1 diabetes. Clin J Am Soc Nephrol. 2009; 4: 62-70.
3. Ghoda T et al. Circulating TNF receptors 1 and 2 predict stage 3 CKD in Type 1 diabetes. J Am Soc Nephrol. 2012; 23: 516-24.
4. Niewczas MA et al. Circulating TNF receptors 1 and 2 predict ESRD in Type 2 Diabetes. J Am Soc Nephrol. 2012; 23: 507-15.
5. Guo G et al. Role of TNFR1 and TNFR2 receptors in tubulointerstitial fibrosis of obstructive nephropathy. Am J Physiol. 1999; 277: F766–F772.
6. Forsblom C et al. Added Value of Soluble Tumor Necrosis Factor Alpha Receptor-1 as a Biomarker of ESRD Risk in Patients With Type 1 Diabetes. Diabetes Care 2014; 37: 1–9.
7. Saulnier et al. Association of Serum Concentration of TNFR1 With All-Cause Mortality in Patients With Type 2 Diabetes and Chronic Kidney Disease: Follow-up of the SURDIAGENE Cohort Published online before print March 12, 2014, doi: 10.2337/dc13-2580.
8. Lopes-Virella MF et al. Baseline markers of inflammation are associated with progression to macroalbuminuria in type 1 diabetic subjects. Diabetes Care 2013; 36: 2317-23. doi: 10.2337/dc12-2521.
9. Skupien et al. Synergism between circulating tumor necrosis factor receptor 2 and HbA1c in determining renal decline during 5-18 years of follow-up in patients with type 1 diabetes and proteinuria. In press: Accepted for publication in Diabetes Care, April 22, 2014.
10. MacIsaac RJ. Markers of and Risk Factors for the Development and Progression of Diabetic Kidney Disease.American Journal of Kidney Diseases 2014; 63: S39–S62.
11. Orchard TJ et al. In the absence of renal disease, 20 year mortality risk in type 1 diabetes is comparable to that of the general population: a report from the Pittsburgh Epidemiology of Diabetes Complications Study. Diabetologia 2010; 53: 2312– 2319.
12. Carlsson AC et al. Soluble TNF Receptors and Kidney Dysfunction in the Elderly. J Am Soc Nephrol. 2014; 25: 1313-1320.

The author
Fergus Fleming
EKF Diagnostic Holdings Plc 
Cardiff, UKwww.ekf-diagnostic.com