Pre-eclampsia is a condition that affects approximately 2–8% pregnancies worldwide and, although the cause is not really understood, is thought to be due to poor function of the placenta. Early signs that create suspicion of pre-eclampsia in the mother typically include hypertension, proteinuria and edema (particularly of the pitting type) of the ankles. Symptoms of more severe pre-eclampsia can include pulmonary edema, headaches, visual disturbance, epigastric/right upper quadrant abdominal pain and vomiting, before the development of seizures (eclampsia). Symptoms in the fetus include fetal growth restriction.
Left untreated, pre-eclampsia is associated with a high risk of adverse outcome for both the mother and fetus. The only treatment is delivery of the baby and the placenta. Diagnosis of pre-eclampsia is challenging because of the vagueness of the symptoms, but becomes suspected with new onset hypertension after 20 weeks’ gestation. The management of women presenting with pre-eclampsia from 37 weeks of gestation is through planned delivery. However, the management of patients with suspected pre-eclampsia earlier in pregnancy involves careful surveillance, and therefore increased use of health resources, balancing the risk to maternal health against the risk of preterm delivery for the fetus. Angiogenic factors, such as vascular endothelial growth factor (VEGF), placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) have shown potential for the diagnosis of pre-eclampsia in cohort studies. Recently, however, the results of study using a stepped-wedge cluster-randomized controlled trial measuring PlGF levels alongside the use of a clinical management algorithm have been published in The Lancet (Duhig KE, et al. Lancet 2019; pii: S0140-6736(18)33212-4). The study involved more than 1000 women with suspected pre-eclampsia and the women were divided into two groups – where the PlGF levels were either made known (revealed PlGF) or not (concealed PlGF). The results showed that in the revealed PlGF group, time to diagnosis fell from 4.1 to 1.9 days compared with the concealed PlGF group and that serious maternal complications fell from 5.3% (24 of 447 women) to 4% (22 of 573 women). The findings from the study, have resulted in NHS England deciding to make the test more widely available, allowing more timely patient management and more appropriate use of resources in high-risk women, so helping to avoid life-threatening complications for both mother and baby as well as providing reassurance when
pre-eclampsia is ruled out.
Standard drug testing is regularly carried out using urine, blood or oral fluid. However, fingerprints present a good alternative, as the sample collection is non-invasive, rapid and safe. Herein, we describe the application of two different testing methods for the detection of cocaine in fingerprint samples.
by Dr Catia Costa, Dr Mahado Ismail and Dr Melanie J. Bailey
Drug abuse in the United Kingdom is on the rise and it is a cause for concern, with widespread financial and social implications [1, 2]. The ever-growing drug and alcohol culture in the UK has led to the implementation of workplace drug testing in many industries, especially those in high-risk operational environments. Consequently, there has been a surge in the demand for drug-screening suppliers to develop faster and more reliable testing. This demand is set to increase the market value of drug and alcohol testing in the UK from £167 million to £231 million by 2019 [3].
Conventionally, drug testing is carried out using biological matrices such as blood, urine and, more recently, oral fluid. These matrices and methods of analysis, although established, present a few problems relating to sample collection and transportation. The collection of blood requires medically trained personnel and sample collection is considered invasive, whereas urine carries privacy concerns. Oral fluid is an alternative matrix used for non-invasive drug testing, although sample collection can be time-consuming. All these three matrices are also biohazardous, making sample storage and transportation a potential issue. The potential use of fingerprints for drug testing has become the subject of many recent publications. Fingerprint samples present a good alternative for drug testing as collection is non-invasive and rapid, and there are no known biohazards associated with the sample. Additionally, the fingerprint pattern can be used for donor identification.
Chemical analysis of fingerprints
The chemical information embedded in a fingerprint sample has been reviewed by many, and several publications have explored the detection of substances such as cocaine [4–6], heroin [7], methadone [8], lorazepam [9], methamphetamines [10], caffeine [11] and cough medicine [12] in fingerprints after administration of the substances. These reports are predominantly based on liquid chromatography-mass spectrometry (LC-MS), which is very well established in the field of toxicology for its quantitative potential as well as its sensitivity and reliability. New advances in the field of mass spectrometry saw the rise of ambient ionization mass spectrometry techniques that allow the sample to be analysed in its native state, under ambient conditions. Examples include desorption electrospray ionization (DESI), liquid extraction surface analysis (LESA) and paper spray, which have been applied to the detection of cocaine and metabolites in fingerprint samples [4–6].
Most of these reports in the literature have looked at fingerprint samples collected after administration of the substances. However, no research has investigated the significance of the detection of these substances compared to a large background population of non-drug users. This is of particular importance as a positive test result may be the outcome of contamination by contact with contaminated surfaces or handling the parent drug rather than ingestion. This directly highlights the need for a sampling strategy that removes any contact residue while providing enough fingerprint material for the analysis.
Detection of cocaine in fingerprints
The detection of cocaine in fingerprints has been studied and reported by Ismail et al. [7]. This study looked at fingerprints collected from the background population (i.e. non-drug users) and from patients at a drug rehabilitation clinic. Both sets of samples were collected as presented and after handwashing, followed by wearing nitrile gloves for 10 minutes. Fingerprint results were supported by oral fluid analysis and patient testimony. Analysis of samples collected from patients (n=13) at the rehabilitation clinic yielded a 100% detection rate for cocaine for samples collected as presented and after handwashing. However, the detection of the cocaine metabolite, benzoylecgonine (BZE), decreased from 94% from samples collected as presented, to 87% for samples collected after handwashing. To evaluate the significance of the results above, fingerprint samples collected from the background population were analysed to investigate the prevalence of these substances in non-drug users. Samples collected as presented (n=99 samples) returned a 13% and 5% detection rate for cocaine and BZE, respectively. After handwashing, cocaine was only detected in 1% of the samples analysed (n=100) and no BZE was present. These findings suggest that cocaine can be detected in the background population owing to environmental exposure (e.g. contact with bank notes). However, after using a handwashing procedure, cocaine and benzoylecgonine were not prevalent. Collection of fingerprint samples after a hand-cleaning procedure is therefore advantageous to reduce potential false-positive rates that can be observed from environmental exposure.
As previously mentioned, the use of chromatographic methods is well established in the field of toxicology. However, such methods often rely on extensive sample preparation and analysis. To overcome this issue we have developed paper spray-mass spectrometry (PS-MS) for the detection of cocaine in under 4 minutes from fingerprints collected from patients seeking treatment at a rehabilitation centre [5]. For this method fingerprints are collected on a triangular piece of paper, which is in turn placed on the paper spray source for analysis. An internal standard, solvent and voltage are applied to the paper, resulting in the extraction and ionization of the fingerprint residues before detection on the mass spectrometer (Fig. 1). The method was evaluated with 239 fingerprint samples collected from drug users at the National Health Service (NHS) rehabilitation clinics and from the background population. A positive result was based on the detection of cocaine or one of its two main metabolites, BZE and ecgonine methyl ester (EME). A 99% true-positive rate was achieved on the samples collected from patients at drug rehabilitation centres, which was supported by standard saliva drug testing and patient testimony. Analysis of samples collected from the general population yielded a 2.5% false-positive rate. This follows from the work by Ismail et al. [7] described above, where in the absence of a hand-cleaning procedure cocaine was detected in the background population. Both studies highlight the need for a well-defined sample collection procedure to eliminate false-positive results while maintaining true-positives.
This method has since its publication been shortened to 30 seconds and it has also been applied to the detection of heroin, morphine, codeine, 6-AM and explosive materials. This highlights the potential for the technique to be on a par with current testing methods that target a wide range of substances.
Fingerprint visualization
Another advantage of using fingerprints for drug testing is the possibility to integrate a fingerprint visualization step for donor identification. This would be of particular benefit for preventing cheating and also in cases of disputed results where one would be able to prove that the results were derived from the correct person. Silver nitrate was used to visualize fingerprint samples collected from drug users by treating the substrate before sample collection. Upon collection, samples were exposed to ultraviolet light to bring out the fingerprint pattern (Fig. 2). Analysis of fingerprint samples collected from drug users after silver nitrate development yielded a 100% detection rate for cocaine, showing great potential for this development step to be included in the fingerprint testing routine.
The future: treatment adherence monitoring
Treatment non-adherence is a well-known problem in the NHS and it is estimated that it can cost over £500 million each year [13]. Thus, the establishment of an adherence monitoring tool could result in substantial savings for the NHS. Fingerprint testing offers the opportunity for remote testing where the samples can be collected by the patient at home and sent to the laboratory for analysis. In cases of non-adherence, medical professionals may intervene and ensure the patient is receiving adequate treatment. This is of particular interest for conditions known to have poor adherence rates such as diabetes, cardiovascular diseases and mental health disorders [14] or for highly infectious diseases such as tuberculosis.
References
1. Barber S, Harker R, Pratt A. Human and financial costs of drug addiction. House of Commons Library 2017.
2. Health matters: preventing drug misuse deaths (GOV.CO.UK2017). Public Health England 2017 (https: //www.gov.uk/government/publications/health-matters-preventing-drug-misuse-deaths/health-matters-preventing-drug-misuse-deaths).
3. Eurofins Workplace Drug Testing launches new holistic ‘wrap around service’ to assist UK plc. Eurofins 2018 (https: //www.eurofins.co.uk/forensic-services/press-releases/uk-growing-drug-culture/).
4. Bailey MJ, Bradshaw R, Francese S, Salter TL, Costa C, Ismail M, Webb RP, Bosman I, Wolff K, de Puit M. Rapid detection of cocaine, benzoylecgonine and methylecgonine in fingerprints using surface mass spectrometry. Analyst 2015; 140(18): 6254–629.
5. Costa C, Webb R, Palitsin V, Ismail M, de Puit M, Atkinson S, Bailey MJ. Rapid, secure drug testing using fingerprint development and paper spray mass spectrometry. Clin Chem 2017; 63(11): 1745–17525.
6. Bailey MJ, Randall EC, Costa C, Salter TL, Race AM, de Puit M, Koeberg M, Baumert M, Bunch J. Analysis of urine, oral fluid and fingerprints by liquid extraction surface analysis coupled to high resolution MS and MS/MS – opportunities for forensic and biomedical science. Anal Methods 2016; 8(16): 3373–3382.
7. Ismail M, Stevenson D, Costa C, Webb R, de Puit M, Bailey M. Noninvasive detection of cocaine and heroin use with single fingerprints: determination of an environmental cutoff. Clin Chem 2018; 64(6): 909–917.
8. Jacob S, Jickells S, Wolff K, Smith N. Drug testing by chemical analysis of fingerprint deposits from methadone-maintained opioid dependent patients using UPLC-MS/MS. Drug Metab Lett 2008; 2(4): 245–247.
9. Goucher E, Kicman A, Smith N, Jickells S. The detection and quantification of lorazepam and its 3-O-glucuronide in fingerprint deposits by LC-MS/MS. J Sep Sci 2009; 32(13): 2266–2272.
10. Zhang T, Chen X, Yang R, Xu Y. Detection of methamphetamine and its main metabolite in fingermarks by liquid chromatography-mass spectrometry. Forensic Sci Int 2015; 248: 10–14.
11. Kuwayama K, Tsujikawa K, Miyaguchi H, Kanamori T, Iwata YT, Inoue H. Time-course measurements of caffeine and its metabolites extracted from fingertips after coffee intake: a preliminary study for the detection of drugs from fingerprints. Anal Bioanal Chem 2013; 405(12): 3945–3952.
12. Kuwayama K, Yamamuro T, Tsujikawa K, Miyaguchi H, Kanamori T, Iwata YT, Inoue H. Time-course measurements of drugs and metabolites transferred from fingertips after drug administration: usefulness of fingerprints for drug testing. Forensic Toxicol 2014: 32(2): 235–242.
13. Trueman P, Taylor D, Lowson K, Bligh A, Meszaros A, Wright D, Glanville J, Newbould J, Bury M, et al. Evaluation of the scale, causes and costs of waste medicines. York Health Economics Consortium/School of Pharmacy, University of London 2010.
14. Cutler RL, Fernandez-Llimos F, Frommer M, Benrimoj C, Garcia-Cardenas V. Economic impact of medication non-adherence by disease groups: a systematic review. BMJ Open 2018; 8(1): e016982.
The authors
Catia Costa*1 PhD, Mahado Ismail2 PhD and Melanie J. Bailey2 PhD
1Ion Beam Centre, University of Surrey, Surrey, GU2 7XH, UK
2Department of Chemistry, University of Surrey, Surrey, GU2 7XH, UK
*Corresponding author
E-mail: c.d.costa@surrey.ac.uk
Acute interstitial nephritis (AIN) may be the cause of more than 15 % of instances of acute loss of kidney function. Diagnosed early, it can often be successfully treated; however, lack of obvious symptoms means it can be easily missed. Left untreated, AIN can progress to chronic kidney disease. Currently, clinical diagnosis is confirmed by invasive biopsy. The development of noninvasive biomarkers is needed for improving early AIN diagnosis and therapeutic outcomes.
Background
Acute interstitial nephritis (AIN) is the inflammation of the kidney interstitium, an area surrounding the renal tubules consisting of fluid and extracellular matrix cells [1]. AIN is generally uncommon (<1 % incidence) in people with no symptoms but is thought to be the cause of more than 15 % of instances of acute loss of kidney function [2, 3].
Causes and symptoms
AIN can be caused by a variety of factors including environmental factors, infection and systemic disease. However, it is most usually seen as a result of adverse reaction to certain medications, such as antibiotics, antivirals, analgesics, gastrointestinal medications, antiseizure medication, diuretics and chemotherapy. When first described, the classic triad of AIN symptoms involved rash, joint pain and eosinophilia, similar to that of an allergic reaction. Fever was also present in 30–50 % of patients. A number of general and vague symptoms, such as nausea, vomiting, fatigue and lack of appetite, were also associated. However, more recently, AIN is more often caused by modern drugs, such as proton pump inhibitors and immune checkpoint inhibitors used in chemotherapy [3] and symptoms, if present, can be very subtle.
Treatment and prognosis
AIN can be treated (unlike other causes of acute loss of kidney function) if detected early by removal of the cause – such as the medication or treatment of the infection. Early treatment results in complete regain of kidney function in approximately 65% of cases, partial resolution in around 20% and irreversible damage in the remainder [4]. Although definitions have been developed and adopted into routine use for acute kidney injury (AKI: abrupt decrease in kidney function occurring over 7 days or less) and chronic kidney disease (CKD: persistence of kidney disease for a period of more than 90 days), there is an increasing awareness that AKI and CKD are not necessarily separate but may represent different stages along a continuum. For patients who develop AKI but then have ongoing pathology, the term acute kidney disease (AKD) has been developed [5]. AIN is an example of this as delayed diagnosis of this condition is more likely to result in incomplete recovery of kidney function and progression to CKD. It has been estimated that 40–60 % of AIN cases progress to CKD and that 2–3 % of CKD cases could be from undiagnosed AIN caused by proton pump inhibitor use [6, 7].
Methods of diagnosis
As we have seen, early diagnosis of AIN is crucial for the best prognosis; however, drug-induced AIN can develop over several days or weeks and the previously described ‘typical’ triad of symptoms is often absent. Physicians need to be aware of a range of more mild and varied symptoms such as flank pain, blood in the urine and joint pain. The main differential diagnosis is acute tubular necrosis (ATN) and the distinguishing features can be seen in Table 2 in Raghavan and Eknoyan’s 2014 paper [4]. Current laboratory tests include testing for markers of tubular dysfunction, which vary depending on the main site of injury; microscopic analysis of urine for the presence of protein, blood and eosinophils; and imaging studies. However, none of these tests are specific for AIN, with the only definitive diagnosis being given by kidney biopsy and even then histological examination needs to be performed by several pathologists.
Biomarkers for AIN
A number of biomarkers are available for the detection of AKI, such as monocyte chemotactic protein-1 (MCP-1), neutrophil gelatinase-associated lipocalin (NGAL), transforming growth factor beta 1 (TGF-β), etc; however, as mentioned by Raghavan and Eknoyan, these have been developed to diagnose AKI based on its definition of increased serum creatinine, which for AIN is too late for the best hope of regaining good kidney function [4 and references therein]. Recently, though, a new study by Moledina and colleagues has identified two new urine biomarkers that improve prebiopsy diagnosis of AIN [7]. The authors postulated that as AIN is caused by certain T-cell subsets, specific T-cell cytokine levels might serve as biomarkers to distinguish AIN from other causes of AKD. Of 218 participants in the study, who had all had kidney biopsy for the evaluation of AKD, 32 were confirmed with AIN and the remaining 186 who did not have AIN were used as controls. After testing 22 selected urine and plasma cytokines, they found two, tumour necrosis factor alpha (TNF-α) and interleukin (IL)-9, that were present at consistently higher levels in the urine of AIN patients and were diagnostic of AIN. The authors conclude “inclusion of urinary TNF-α and IL-9 improves discrimination over clinicians’ prebiopsy diagnosis and currently available tests for AIN diagnosis” [7]. In another recent paper from the same group, the authors demonstrate that the use of urinary TNF-α and IL-9 biomarkers also allows the differentiation of AIN over ATN [8].
Summary
Diagnosis of drug-induced AIN at a stage early enough to limit irreversible kidney damage is challenging because of a lack of conclusive symptoms and the gold standard diagnosis of kidney biopsy is invasive, not suitable for all patients and fraught with low inter-rater agreement. The results of the study by Moledina et al. demonstrate that testing for urine biomarkers TNF-α and IL-9 is a useful addition to a clinician’s prebiopsy diagnosis and might be able to eventually replace the need for kidney biopsy. The use of these biomarkers may be a welcome step for maximizing the chances of complete kidney function recovery and limiting the number of patients who progress to CKD.
References
1. Zeisberg M, Kalluri R. Physiology of the renal interstitium. Clin J Am Soc Nephrol 2015; 10(10): 1831–1840.
2. Brewster UC, Rastegar A. Acute Interstitial Nephritis. In: National Kidney Foundation’s primer on kidney diseases, eds Gilbert SJ, Weiner DE, Bomback AS, Parazella MA, Tonelli M, 7th edn; pp320–325. Elsevier 2017. ISBN 978-0323477949.
3. Mamlouk O, Selamet U, Machado S, Abdelrahim M, Glass WF, et al. Nephrotoxicity of immune checkpoint inhibitors beyond tubulointerstitial nephritis: single-center experience. J Immunother Cancer 2019; 7(1): 2.
4. Raghavan R, Eknoyan G. Acute interstitial nephritis – a reappraisal and update. Clin Nephrol 2014; 82(3): 149–62.
5. Chawla LS, Bellomo R, Bihorac A, Goldstein SL, Siew ED, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol 2017; 13(4): 241–257.
6. Lazarus B, et al. Proton pump inhibitor use and the risk of chronic kidney disease. JAMA Intern Med 2016; 176(2): 238–246.
7. Moledina DG, Wilson FP, Pober JS, Perazella MA, Singh N, et al. Urine TNF-α and IL-9 for clinical diagnosis of acute interstitial nephritis. JCI Insight 2019; 4(10): pii: 127456.
8. Moledina DG, Parikh CR. Differentiating acute interstitial nephritis from acute tubular injury: a challenge for clinicians. Nephron 2019; doi: 10.1159/000501207 [Epub ahead of print].
Graft-versus-host disease is a serious complication following hematopoietic stem cell transplantation (HSCT), with a high mortality rate. A clearer understanding of the molecular pathogenesis may allow robust biomarker identification and improved therapeutic options. MicroRNAs (miRNAs) are short non-coding regulatory RNAs that are expressed in both tissue and body fluids, and show great potential as clinically translatable biomarkers. Here we discuss the field of miRNA biomarker discovery in the setting of HSCT.
by Dr Rachel E. Crossland and Prof. Anne M. Dickinson
Allogeneic hematopoietic stem cell transplant and graft-versus-host disease
Allogeneic hematopoietic stem cell transplant (allo-HSCT) is a curative treatment for many blood cancers. It is based on the transplant of hematopoietic blood and marrow stem cells from related or unrelated donors, and over 17 000 allo-HSCT transplants a year are carried out in Europe. The therapy is curative due to the properties of subsets of donor-derived lymphocytes, including T-cells and natural killer cells, that are able to eradicate residual malignancy due to their ‘graft-versus-leukemia’ (GvL) effects. However, T-cells can also give rise to a life-threatening complication, called graft-versus-host disease (GvHD).
GvHD affects 40–70 % of HSCT patients, and severe disease is associated with 40–60 % mortality. The pathology of GvHD is not completely understood, but has been generally attributed to three main stages:
Acute GvHD (aGvHD) typically occurs within the first 100 days following transplantation and primarily presents in the skin, liver and gastrointestinal tract as an erythematous maculopapular rash, elevated bilirubin, and diarrhoea and vomiting, respectively [1]. Chronic GvHD (cGvHD) has a more delayed onset, and is a multi-organ allo- and auto-immune disorder that most frequently affects the skin, lung, mouth, liver, eye, joints and gastrointestinal tract causing a plethora of co-morbidities including cardiovascular, gastrointestinal, hepatic, pulmonary, endocrine, bone and joint disorders, infections and secondary malignancies. GvHD is commonly treated with immunosuppressants, which increase the patient’s susceptibility to life-threatening infections. Therefore, survival rates after allo-HSCT have not improved for over two decades, owing to major complications such as infections, GvHD and relapse of malignant disease. To date, GvHD can be well characterized by established and clinically validated GvHD grading scales and measurements of the National Institute of Health (NIH) Consensus classification. However, there is a lack of understanding of the immunobiology and metabolic triggers that cause the development and further perpetuation of GvHD, especially cGvHD and subsequent co-morbidity.
GvHD and biomarkers
Biomarkers are being increasingly used in the prediction, prognosis and diagnosis of diseases and are now being validated for prediction of outcome in patients with GvHD. Predicting and preventing GvHD would allow clinicians to develop of risk-adapted clinical protocols, encourage a curative GvL response and improve outcomes, including transplant survival rates and long-term complications. However, despite the frequency and significance of GvHD, there are currently no early diagnostic or predictive markers that have been validated for use in clinic. This may be attributed to a lack of understanding of the molecular pathobiology of aGvHD on a systemic level. Determining the molecular pathways involved at initiation of aGvHD will identify novel targets for therapeutic intervention, and these factors may have the potential to act as biomarkers for aGvHD.
MicroRNAs as biomarkers
MicroRNAs (miRNAs) represent a promising source of biomarkers for GvHD because they play critical roles in the development and function of the immune system and in transplant biology (Fig. 1). MiRNAs represent a family of small (19–24 nucleotide) non-coding RNAs, which affect the regulation of gene expression in eukaryotic cells by binding to the 3´-untranslated region of target messenger RNAs [2]. They are predicted to target around 50 % of all genes and play an important role in fundamental cellular processes such as development, stem cell division, apoptosis and cancer. MiRNAs represent ideal candidates for biomarker identification in GvHD as they can be assessed using accurate and sensitive technology (e.g. NanoString/qRT-PCR), quantified in bodily fluids that require minimally invasive sample collection (e.g. serum/urine) and further investigated for biological function (e.g. target protein identification) that may expand upon our understanding of GvHD pathology. Although the field of GvHD-related miRNA research is in its infancy, recent studies have demonstrated an emerging role for miRNAs as GvHD biomarkers.
MiRNAs as biomarkers for GvHD
MiR-155 was one of the first miRNAs to be associated with the regulation of aGvHD. This miRNA is a critical regulator of inflammation, as well as adaptive and innate immune responses. In 2012, Ranganathan et al. demonstrated upregulation of miR-155 in the T-cells of mice and patients developing aGvHD following HSCT [3]. Serum expression levels also correlated with GvHD severity, and serum IFN-gamma, IL-17 and IL-9 levels, suggesting the potential of miR-155 as a biomarker for aGvHD diagnosis, and as a therapeutic target. It has since been demonstrated that miR-155 expression in both donor CD8+ T-cells and conventional CD4+ CD25− T-cells is pivotal for aGvHD pathogenesis, and drives a pro-inflammatory Th1 phenotype in donor T-cells [4].
MiR-146 is increasingly being recognized as a ‘fine-tuner’ of cell function and differentiation in both innate and the adaptive immunity. MiR-146a controls innate immune cell and T-cell responses, and directly targets two adapter proteins in the nuclear factor-kappa B (NF-κB) activation pathway; tumour necrosis factor (TNF) receptor-associated factor 6 (TRAF6) and IL-1 receptor-associated kinase 1 (IRAK1) [5]. In addition, the survival and maturation of human plasmacytoid dendritic cells that are involved in GvHD can be regulated by miR-146a. With regard to GvHD, miR-146a has been shown to be upregulated in the T-cells of nice developing aGvHD, and transplanting miR-146a–/– T-cells causes increased GvHD severity, elevated TNF serum levels and reduced survival [6]. Interestingly, Stickel et al. observed downregulation of miR-146a shortly following allo-HCT in mice (day 2), followed by upregulation in T-cells later in the aGvHD reaction (days 6 and 12), which they hypothesized may be a rescue mechanism to counteract inflammation [6]. Expression of miR-146a has since been identified to show a statistical interaction with expression of miR-155 in the peripheral blood of allo-HSCT patients before disease onset, and this interaction was predictive of aGvHD incidence, further implicating its potential as a GvHD biomarker [7].
Serum expression of miR-29a has recently been implicated as a potential biomarker for GvHD. Ranganathan et al. showed in two independent cohorts that miR-29a is significantly upregulated in allo-HSCT patients at aGvHD onset compared with non-aGvHD patients, and as early as 2 weeks before symptomatic disease onset compared to time-matched controls [8]. Further investigation into the function of miR-29a showed that it binds to and activates dendritic cells, via toll-like receptor (TLR)7 and TLR8, resulting in the activation of the NF-κB pathway and secretion of pro-inflammatory cytokines. Treatment with locked nucleic acid anti-miR-29a significantly improved survival in a mouse model of aGvHD, while retaining GvL effects [8].
In 2013 an elegant study by Xiao et al. investigated miRNA expression profiles in the plasma of patients with aGvHD, compared to patients with no aGvHD, using a qRT-PCR array to include 345 miRNAs [9]. The study identified a final signature of four miRNAs (miR-423, miR-199-3p, miR-93*, and miR-377) that significantly predicted for aGvHD at 6 weeks post-HSCT, before the onset of symptoms. Furthermore, the model was associated with disease severity and poor overall survivall [9]. Gimondi et al. have also profiled circulating miRNA expression using a qRT-PCR platform, based on samples collected 28 days post-HSCT [10]. They detected 27 miRNAs that could collectively discriminate between aGvHD and non-aGvHD. MiR-194 and miR-518f were significantly upregulated in patients who later developed aGvHD, and these miRNAs were predicted to target critical pathways implicated in aGvHD pathogenesis [10]. Our laboratory has used NanoString technology to comprehensively profile the expression of n=799 mature miRNAs in the serum of patients who had undergone HSCT, to identify miRNAs with altered expression at aGvHD diagnosis (Fig. 2) [11]. Assessment of selected miRNAs was also replicated in independent cohorts of serum samples taken at aGvHD diagnosis and before disease onset to assess their prognostic potential. Expression analysis identified 61 miRNAs that were differentially expressed at aGvHD diagnosis, and miR-146a, miR-30b-5p, miR-374-5p, miR-181a, miR-20a, and miR-15a were significantly verified in an independent cohort. MiR-146a, miR-20a, miR-18, miR-19a, miR-19b, and miR-451 were also differentially expressed 14 days post-HSCT, before the onset of symptoms, in patients who later developed aGvHD. High miR-19b expression was associated with improved overall survival, whereas high miR-20a and miR-30b-5p were associated with lower rates of non-relapse mortality and improved overall survival [11]. Collectively, these miRNA profiling studies highlight that circulating biofluid miRNAs show altered expression at aGvHD onset and have the capacity to act as independent markers for prediction, prognosis and diagnosis of GvHD.
Future directions
Despite greater recognition of the potential for miRNAs as clinically adaptable biomarkers, they have not yet reached translation to the clinic. This is predominantly because of the lack of reproducibility and independent validation to date. Indeed, owing to the high degree of variability in factors when designing and performing miRNA profiling experiments, which may be attributed to clinical (patient characteristics, sampling time points and type of body fluid analysed), technical (sample preparation, miRNA profiling platform and spectrum of miRNAs profiled) and analytical (normalization strategy) factors, progress has been slow in realizing their full potential. Despite contradictory research results on the biological basis of GvHD, low patient cohorts in single transplant centre studies, insufficient characterization of GvHD and lack of understanding and knowledge of GvHD’s impact on the immune system, miRNA biomarkers continue to show promise, but many studies are still in their infancy. Future progress relies on collaboration between research groups, focusing on standardization of the samples, protocols and technologies used, which will greatly improve the reproducibility of findings allowing for extended validation of miRNAs of interest. The ultimate aim will be to diagnose GvHD and predict outcome before the onset of clinical symptoms, allowing for earlier therapy and personalized treatments and leading to reduced mortality and morbidity outcomes.
References
1. Shlomchik WD. Graft-versus-host disease. Nat Rev Immunol 2007; 7(5): 340–352.
2. Stefan LA, Phillip DZ. Diversifying microRNA sequence and function. Nature Reviews Mol Cell Biol 2013; 14(8): 475–488.
3. Ranganathan P, Heaphy CEA, Costinean S, Stauffer N, Na C, Hamadani M, Santhanam R, Mao C, Taylor PA, et al. Regulation of acute graft-versus-host disease by microRNA-155. Blood 2012; 119(20): 4786–4797.
4. Zitzer NC, Snyder K, Meng X, Taylor PA, Efebera YA, Devine SM, Blazar BR, Garzon R, Ranganathan P. MicroRNA-155 modulates acute graft-versus-host disease by impacting T cell expansion, migration, and effector function. J Immunol 2018; 200(12): 4170–4179.
5. Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci USA 2006; 103(33): 12481–12486.
6. Stickel N, Prinz G, Pfeifer D, Hasselblatt P, Schmitt-Graeff A, Follo M, Thimme R, Finke J, Duyster J, et al. MiR-146a regulates the TRAF6/TNF-axis in donor T cells during GvHD. Blood 2014; 124(16): 2586–2595.
7. Atarod S, Ahmed MM, Lendrem C, Pearce KF, Cope W, Norden J, Wang XN, Collin M, Dickinson AM. miR-146a and miR-155 expression levels in acute graft-versus-host disease incidence. Frontiers in immunology. 2016; 7: 56.
8. Ranganathan P, Ngankeu A, Zitzer NC, Leoncini P, Yu X, Casadei L, Challagundla K, Reichenbach DK, Garman S, et al. Serum miR-29a is upregulated in acute graft-versus-host disease and activates dendritic cells through TLR binding. J Immunol 2017; 198(6):2500–2512.
9. Xiao B, Wang Y, Li W, Baker M, Guo J, Corbet K, Tsalik EL, Li QJ, Palmer SM, et al. Plasma microRNA signature as a noninvasive biomarker for acute graft-versus-host disease. Blood 2013; 122(19): 3365–33675.
10. Gimondi S, Dugo M, Vendramin A, Bermema A, Biancon G, Cavane A, Corradini P, Carniti C. Circulating miRNA panel for prediction of acute graft-versus-host disease in lymphoma patients undergoing matched unrelated hematopoietic stem cell transplantation. Exp Hematol 2016; 44(7): 624–634.e1.
11. Crossland RE, Norden J, Juric MK, Green K, Pearce KF, Lendrem C, Greinix HT, Dickinson AM. Expression of serum microRNAs is altered during acute graft-versus-host disease. Front immunol 2017; 8: 308.
The authors
Rachel E. Crossland* PhD and Anne M. Dickinson PhD
Haematological Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK
*Corresponding author
E-mail: Rachel.crossland@ncl.ac.uk
Twitter: @RECrossland
February | March 2025
Beukenlaan 137
5616 VD Eindhoven
The Netherlands
+31 85064 55 82
info@clinlabint.com
PanGlobal Media is not responsible for any error or omission that might occur in the electronic display of product or company data.