A priori TIH must result from excessive saliuresis and/or water reabsorption. We hypothesized that pathways regulating the thiazide-sensitive sodium-chloride cotransporter NCC and the water channel aquaporin-2 (AQP2) may be involved. Our aim was to assess whether patients with TIH would show evidence of altered NCC and AQP2 expression in urinary extracellular vesicles (UEVs), and also whether abnormalities of renal sodium reabsorption would be evident using endogenous lithium clearance (ELC).Thiazide diuretics are among the most widely used antihypertensive medications worldwide. Thiazide-induced hyponatremia (TIH) is 1 of their most clinically significant adverse effects. Blood and urine samples were donated by patients admitted to hospital with acute symptomatic TIH, after recovery to normonatremia, and also from normonatremic controls on and off thiazides. Urinary extracellular vesicles were isolated and target proteins evaluated by western blotting and by nanoparticle tracking analysis. Endogenous lithium clearance was assessed by inductively coupled plasma mass spectrometry.2 relative to appropriate control groups; smaller differences in NCC and AQP2 expression persisted after recovery from TIH. These findings were confirmed by nanoparticle tracking analysis. Renal ELC was lower in acute TIH compared to that in controls and convalescent case patients.Analysis of UEVs by western blotting showed that patients with acute TIH displayed reduced total NCC and increased phospho-NCC and AQP2 expression would be expected to result in saliuresis and water reabsorption in TIH patients. This study raises the possibility that UEV analysis may be of diagnostic utility in less clear-cut cases of thiazide-associated hyponatremia, and may help to identify patients at risk for TIH before thiazide initiation.Reduced NCC expression and increased AQP Thiazide diuretics have been used in the management of hypertension for more than half a century.5A priori TIH must result from excessive saliuresis and/or water reabsorption (or ingestion). It therefore seems likely that excessive saliuresis and/or water reabsorption (or ingestion) occurs via altered regulation of NCC and/or aquaporin-2 (AQP2). As thiazide diuretics have not, to the best of our knowledge, been reported to produce saliuresis with a urinary sodium concentration greater than that of plasma, this suggests that excessive water reabsorption (or ingestion) is a fundamental part of the pathogenesis of TIH. Our aim in this study was to probe the molecular mechanisms underlying TIH by investigating whether patients with TIH exhibit evidence of altered regulation of NCC and AQP2.The mechanisms underlying TIH remain unclear. Risk factors for TIH include advanced age, reduced body mass, and concurrent use of other medications that impair water excretion.1 and NCC2 vary by only 1 amino acid.3, however is 9 amino acids longer; it constitutes approximately 40% of NCC is the human nephron. Regulation of NCC occurs principally by 2 mechanisms: regulation of apical membrane abundance (type 1 regulation) and regulation of transporter kinetics (type 2 regulation), the latter being controlled by phosphorylation of key N-terminal serine and threonine residues .2 pathway in the collecting duct.2, increasing the water permeability of the collecting duct.2 (PGE2) is a key modulator of this pathway,2-specific prostaglandin transporter (PGT). A recent study has suggested modest genetic associations with TIH, including aberrant PGE2 reuptake in the distal nephron that may increase AQP2-mediated water reabsorption in a subset of patients who carry a variant within SLCO2A1, the gene encoding PGT.The sodium-chloride cotransporter  is located in the renal cortex; therefore thiazides reduce maximal urinary diluting ability by increasing sodium excretion without affecting the renal medullary sodium gradient required for water reabsorption from the collecting duct.Urinary extracellular vesicles (UEVs) are spherical, structured membrane vesicles that are formed by cells throughout the nephron.22Because lithium is absorbed along with sodium throughout the nephron, it is possible to use measurement of lithium clearance to assess tubular sodium reabsorption. Renal lithium clearance likely reflects mostly proximal tubular sodium reabsorption, because this is where the majority of sodium is reabsorbed.2, we report analysis of urinary extracellular vesicle NCC and AQP2 in TIH patients and controls. We also report assessment of renal tubular sodium reabsorption in TIH patients by measurement of ELC.To fulfill our aim of investigating whether patients with TIH exhibit evidence of altered regulation of NCC and AQPThis study was conducted in line with the standards of ICH/Good Clinical Practise sections 8.2.8 in adherence to the Declaration of Helsinki and was approved by the UK National Research Ethics Committee (reference 11/EM/0233). Informed consent was given by all participants. Between April 2012 and August 2015, a daily search of the biochemistry database of all patients admitted to the department of internal medicine at Nottingham University Hospitals NHS Trust, UK, was undertaken to identify those patients with serum sodium <130 mM (cases). Patients were reviewed by the investigators to establish those whose hyponatremia was due to a thiazide diuretic (TIH). Normonatremic thiazide and nonthiazide controls (serum sodium 135−145 mM) were identified by primary care surgeries in Nottinghamshire and matched as closely as possible to the case patients for age, sex, comorbidities, and polypharmacy. Patients with TIH were also assessed in the outpatient clinic 2 months after thiazide cessation (termed normonatremic TIH case patients off thiazides). The patients described here were a subset of those previously reportedSecond-morning spot urine samples (30 ml) were collected in sterile containers from TIH patients and control groups . Proteasg for 5 minutes at room temperature. Frozen urine samples were thawed on ice, 1 ml dithiothreitol  was added, extensively vortexed and incubated for 30 minutes at 37 °C before UEV isolation. All samples were centrifuged (2500 g for 15 minutes at 4 oC) for urinary cell removal. Cell pellet was suspended in lysis buffer  and stored at −80 oC. Further filtration of supernatant using 0.22-μm filters  was undertaken to remove debris. A 25-ml quantity of filtered supernatant was centrifuged at 15,000 g for 30 minutes at 4 °C. The urine supernatants were then added to Vivaspin (USA) nanomembrane concentrators and centrifuged at 4800 g for 1 hour at 4 °C. Concentrated UEVs were treated with 50 μl dithiothreitol, added directly into the filters, and incubated for 10 minutes at 37 °C. Isolated urinary extracellular vesicles were directly suspended in lysis buffer and stored at −80 °C. Total urine and flow-through fractions were also stored at −80 oC and −20 oC. Total soluble protein concentration was quantified with bicinchoninic acid assay .Urinary nanovesicles, including UEVs, were isolated from both fresh and frozen urine by Ultrafiltration . Before processing of urine samples, nanomembrane concentrators were washed with phosphate-buffered saline solution to remove glycerol and other preservatives and then centrifuged at 2500 2) acrylamide gels. Sodium dodecyl sulfate−polyacrylamide gel electrophoresis was performed on polyvinylidene difluoride membrane . Urinary extracellular vesicle AQP2 was detected by probing the blots with 1:500 diluted anti-AQP2 antibody  followed by anti-rabbit IgG horseradish peroxidase (HRP)−conjugated antibody (Sigma-Aldrich). Urinary extracellular vesicle NCC3 was probed with 1:500 diluted anti-NCC primary antibody (Millipore) followed by anti-rabbit IgG HRP−conjugated antibody (Sigma-Aldrich). Urinary extracellular vesicles NCC1 and NCC2 were probed with 1:500 diluted anti-NCC primary antibody  followed by anti-rabbit IgG HRP-conjugated antibody (Sigma-Aldrich). Urinary extracellular vesicle phospho-NCC  was detected by probing blots with anti–phospho-NCC antibodies , each at 1:500 (0.2–1.2 μg/ml) followed by anti-sheep IgG HRP-conjugated antibody (Sigma-Aldrich). To detect specific phosphorylated forms of NCC, the corresponding nonphosphorylated peptide was incubated with primary antibody (10 μg/ml). Urinary extracellular vesicle PGT was probed with 1:200 diluted anti-PGT primary antibody  followed by anti-rabbit IgG HRP-conjugated antibody (Sigma-Aldrich). The anti-AQP2 and NCC antibodies used target intracellular domains of AQP2 and NCC. Vesicles did not undergo prior permeabilization. Urinary cell pellet was used as a negative control for western blots. Urinary extracellular vesicle ALIX was detected by probing the blots with 1:1000 diluted anti-ALIX antibody  followed by anti-rabbit IgG HRP-conjugated antibody (Sigma-Aldrich). Western blot data were corrected for ALIX as indicated in the legends of P value of <0.05 was considered significant.A total of 25 μg soluble protein per sample was electrophoresed on 7.5 % (NCC and phospho-NCC), 10% (apoptosis-linked gene 2-interacting protein X [ALIX] and PGT) and 12.5%  is a light-scatter microscopy method of tracking microparticles and nanoparticles on the basis of direct and real-time tracking of the particles’ Brownian movement, which results in a description of the particle size and concentration distribution in a given solution. Nanoparticle tracking analysis can be used to count and measure specific subgroups of nanoparticles using fluorescent antibodies against nanoparticle proteins, including extracellular vesicles derived from kidney cells in culture and in urine.2 and NCC-expressing UEVs were analyzed using the NanoSight LM 10 instrument . The analysis settings were optimized and kept constant between samples, and each video was analyzed to give the mean, mode, median, and estimated concentration for each particle size. All experiments were carried out at a 1:1000 dilution of unprocessed urine, yielding particle concentrations in the region of 1.3 × 108 particles/ml in accordance with the manufacturer’s recommendations. For fluorescent NTA analysis, a 532-nm (green) laser diode excited the fluorescent-loaded ECVs with a long-pass filter (430 nm). For quantification of AQP2  and NCC , primary antibodies were fluorescent-labeled by conjugation to quantum dots. All samples were analyzed in triplicate to give 1 value per patient. Data for NCC and AQP2 were normalized by urinary creatinine.Both AQP.Serum and urine samples were prepared for analysis with a 1:25 dilution in 0.5% nitric acid  containing 0.05% Triton-X 100 (Romil) and 40 ng/ml beryllium  as an internal standard. Measurements were performed on an Agilent 7900 ICP-MS  operated in no-gas mode with the radiofrequency generator power set to 950 W giving a “cold” plasma. Lithium was measured at m/z ratio of 7 and was normalized to the beryllium internal standard at m/z 9. Calibration solutions were prepared by diluting a 1000-μg/ml lithium standard  in 2% nitric acid (Romil) to give concentrations of 0.0352, 0.141, 0.563, 2.25, 9, and 36 μmol/l. Seronorm trace elements urine quality control materials  and an in-house serum control were analyzed in each batch2, NCC3, and PGT was undertaken in healthy volunteers to establish (i) the principal urinary fraction(s) these transporters are excreted in UEVs, cell pellet, or total urine, and (ii) the optimal method of storage of urine samples for UEV analysis (fresh vs. frozen urine stored at either −20oC or −80 oC).Western blot analysis of AQP2, NCC3, and PGT was much greater in UEVs than either cell pellet or total urine, confirming that UEVs are the most appropriate fraction of urine to study  was significantly more abundant in acutely hyponatremic TIH case patients on thiazides compared to normonatremic controls on thiazides. Despite reduction in PGT in TIH case patients following cessation of thiazide and recovery to normonatremia, PGT expression was still significantly greater than in normonatremic controls off thiazides d.3 and AQP2 excretion between these patient groups. Nanoparticle tracking analysis has been used previously to quantify the abundance of UEVs containing AQP232-positive UEVs in both acutely hyponatremic and convalescent normonatremic states compared to controls  was undertaken. Patients acutely hyponatremic on thiazides with TIH displayed significantly greater phosphorylation at T55, T60, and S91 than either convalescent normonatremic TIH case patients off thiazides or normonatremic controls on thiazides .Renal endogenous lithium clearance (ELC) was lower in acutely hyponatremic TIH case patients on thiazides compared to convalescent TIH case patients on recovery to normonatremia following thiazide cessation . The ELC2 in their UEVs relative to appropriate control groups.Here we report a noninvasive method of obtaining, preserving, and analyzing urinary extracellular vesicles from volunteer patients with thiazide-induced hyponatremia who were admitted to an acute internal medicine department. In addition, analysis of distal nephron sodium, water, and prostaglandin transporters contained within the extracellular vesicles suggests a possible mechanism that may underlie the pathophysiology of TIH. These data add to what has previously been reported2, and PGT. Furthermore, we demonstrate that it is not necessary to analyze only fresh urine samples, and that samples frozen with protease inhibitor are equally suitable for use. This is important, because if analysis were limited to fresh samples, the time-consuming sample processing and immediate exosome isolation would make the study of acute medical conditions such as TIH very difficult. Because acquisition of a spot urine sample, addition of a protease inhibitor tablet, and storage in a freezer can be performed by the majority of clinical and research staff with very limited training required, such sample acquisition from acute medical patients becomes a much more feasible proposition.We have shown that the extracellular vesicles fraction is the most appropriate component of human urine with which to study the expression of NCC, AQPA priori TIH must result from excessive saliuresis and/or water reabsorption. As thiazide diuretics have not been observed to produce saliuresis with urinary sodium concentration greater than those of plasma, excess water reabsorption (or ingestion) is likely to be a fundamental part of TIH pathophysiology. Reduced renal expression of NCC and increased expression of AQP2 in acute TIH would support the hypothesis that the pathophysiology of TIH results from a combination of both of these processes. We have successfully identified both NCC1 and NCC2, NCC3 and determined the phosphorylation status of 3 key N-terminal phosphorylation sites known to regulate NCC activity from the urinary extracellular vesicles. In addition, reduced endogenous lithium clearance (ELC) in acute TIH supports increased proximal sodium reabsorption reflecting augmented distal nephron sodium loss, and so is complementary to the UEV data.The increased N-terminal phosphorylation state of NCC in acute TIH suggests some degree of physiological compensation to excessive distal nephron sodium loss, by attempting to maximally activate what NCC exists in the distal convoluted tubule. Why type 1 regulation of NCC  should be impaired in TIH but type 2 regulation  should remain intact is unclear. It is notable that thiazides inhibit NCC by binding to the cotransporter from the luminal membrane, and 1 hypothesis may be that the physical binding of thiazides to NCC induces a conformational change that targets the cotransporter for endocytosis and degradation in patients susceptible to TIH. Alternatively, saliuresis and resulting kaliuresis and trend to hypokalemia seen in acute TIH patients may drive phosphorylation and activation of remaining NCC.Thiazides reduce the ability of the late diluting segment  to generate solute-free water directly, and also act by reducing effective vascular volume and thus solute delivery from the end-proximal tubule.Measuring differences in the UEV profile of acutely hyponatremic patients may also have clinical utility as an aid to TIH diagnosis where other potential contributors to hyponatremia exist. The use of UEV analysis as a diagnostic tool would require further studies to identify whether acute differences in UEV profiling exist in other causes of hyponatremia and whether profiling of a panel of UEV markers could reliably differentiate 1 cause of hyponatremia from another.2 uptake, 1 of the most important physiological regulators of distal nephron water reabsorption.SLCO2A1 (p.A396T), the gene that encodes PGT, was also recently reported to show modest association with TIH.We chose also to study PGT because it is constitutively expressed in the collecting duct, where it mediates PGE2 could potentially identify patients at higher risk for TIH. These individuals could then be offered alternative antihypertensive therapy, for example, medications from the angiotensin-converting enzyme inhibitor or calcium channel antagonist group; or, if a diuretic is required, an alternative loop or potassium-sparing type could be selected instead of a thiazide.That differences in the extracellular vesicle profile of convalescent normonatremic TIH cases exist 2 months after thiazides have been discontinued suggests that patients who are predisposed to TIH may exhibit these urinary extracellular vesicle characteristics before they are exposed to thiazides. Although a prospective study would be required to confirm this observation, it raises the possibility that pre-prescription profiling of the relative abundance of NCC to AQP2 expression in their UEVs, which would be expected to produce excessive saliuresis and water resorption. Reduced ELC also suggests compensatory increased proximal sodium reabsorption in TIH. That differences in the renal transporters responsible for these processes can be detected in urinary extracellular vesicles both acutely and following recovery from TIH raises the possibility that such techniques may have utility in prospectively identifying those individuals at high risk for TIH and in aiding the acute diagnosis of TIH in cases that are confounded by comorbidity and/or polypharmacy.In summary, we show here that TIH patients display reduced NCC expression and increased AQPAll the authors declared no competing interests.


P = 0.0025) and a high likelihood of encouraging others to be screened for their CHD risk , compared to the absence of self-reported EHR access to view CHD risk information. We thereby used EHR access trends to identify individuals who may function as disseminators of CHD risk information in social networks, compared to individuals on the periphery of their social networks who did not exhibit this behavior. Partnering with such individuals could amplify CHD health promotion.We investigated electronic health record (EHR) access as an indicator of cardiovascular health promotion by patients in their social networks, by identifying individuals who viewed their coronary heart disease (CHD) risk information in the EHR and shared this information in their social networks among various spheres of influence. In a secondary analysis of the Myocardial Infarction Genes trial, Olmsted County MN residents  at intermediate CHD risk were randomized to receive their conventional risk score  alone or also their genetic risk score . We assessed self-reported and objectively quantified EHR access via a patient portal at three and six months after risk disclosure, and determined whether this differed by GRS disclosure. Data were analyzed using logistic regression and adjusted for sociodemographic characteristics, family history, and baseline CRS/GRS. Self-reported EHR access to view CHD risk information was associated with a high frequency of objectively quantified EHR access (71(10) versus 37(5) logins; Clinical Trial Registration: Myocardial Infarction Genes (MI-GENES) Study, NCT01936675, https://clinicaltrials.gov/ct2/show/NCT01936675. In one study, the leading reason  for accessing one's EHR was to monitor one's health  . The secSuch sharing of health information may promote adoption of preventive measures, particularly for common chronic diseases, in social networks. Furthermore, such adoption may be amplified when risk estimates include genetic susceptibility variants. Multi-locus genetic risk scores for CHD, for example, refine disease risk  and predPrior studies of EHR access largely centered on patient's perspectives on the importance and security of having access to their EHR, as well as the details of why and how patients use the EHR, and with whom patients share data from their EHR . Some reIn this secondary analysis of the Myocardial Infarction Genes (MI-GENES) trial, we hypothesized that individuals who self-reported EHR access to seek their CHD risk information actually accessed the EHR frequently and shared the gleaned health information widely in their social networks. We then developed a conceptual model of the role of these individuals in dissemination of health information in their social networks.22.1In the MI-GENES study  Fig. 1)Fig. 1), 2.2Participants' recall of their disclosed CHD risk was assessed at three and six months after risk disclosure using the question, “Out of 100 people like you, how many individuals would have a heart attack over the next 10 years?”http://hints.cancer.gov) from 2008 and 2005, respectively. Questions about signing up for access and using the portal to obtain their CHD risk information were added to identify individuals who access the portal to review their CHD risk.In the EHR access survey administered three and six months after risk disclosure (Appendix Table A.1), questions regarding communicating with a doctor's office and keeping track of personal health information were adapted from the National Cancer Institute's Health Information National Trends Surveys , questions regarding seeking information outside of the EHR about how personal health habits and genetics affect CHD risk were adapted from The ‘Information Sharing’ survey was completed at three and six months post-disclosure (Appendix Table A.3). A question regarding discussing CHD risk with others was adapted from For the ‘Social Network’ survey administered at baseline and at three and six months post-disclosure (Appendix Table A.3), questions about having friends or family with whom they discussed health information or having community organizations that provided health information were adapted from the HINTS 2005 survey.2.3Historical data from the medical record were used to quantify frequency of EHR access over the course of the study. The actual number of discrete instances in which each participant's personal, private, and unique username and password login credentials were manually entered on the internet website or in the Mayo Clinic mobile patient application to access the patient's EHR, from the initial visit until at least 12 months post-disclosure (with an average of 14 months post-disclosure), was termed ‘TotalLogin’ (‘Expansion of Results’ in Appendix C) (Appendix Tables A.4–7). Associations between TotalLogin and favorable survey question responses were determined using linear regression (Appendix Table A.4).2.4We developed a conceptual model of the role of individuals who may extensively share health information in social networks. Connecting lines were used to represent EHR access via a patient portal by study participants. In studies of social networks, the quantified frequency of accessing health information is represented by the width of the connecting line (or ‘tie’) between an individual and the source of that information , and cap2.5P-value of <0.05 was used to determine statistical significance in this exploratory secondary analysis. Post-hoc power analyses and multivariate analyses were also performed .Survey data were stored using the Research Electronic Data Capture (REDCap)  software3Participants in both study groups had similar sociodemographic characteristics ; the maj3.1One third of study participants correctly recalled their disclosed CHD risk, with no significant difference between the CRS and GRS groups .3.2P = 0.0029) and CRS participants (69(12) versus 29(6); P = 0.0063), EUCRs had higher quantified EHR access frequencies than NEUCRs . Individuals who reported accessing the patient portal to review their CHD risk information at three or six months after risk disclosure (in response to question 5 in Appendix Table A.1) were labelled as “EHR Users for CHD Risk” (EUCRs). Individuals who self-reported NOT logging into the patient portal to review their CHD risk information were labelled as “NOT EHR Users for CHD Risk” (NEUCRs). There was no difference in risk recall between EUCRs and NEUCRs (data not shown). Self-reported EHR access associated with objectively quantified frequency of EHR access  (‘Expansn NEUCRs . This su3.3P = 0.0030) (data not shown), and tended to have a wider information sharing radius than NEUCRs three months after CHD risk disclosure  (P = 0.0870). Quantified EHR access frequency also associated with self-reported internet use outside of the EHR and information sharing via interpersonal communication in social networks   a, with anetworks . Thus, b3.4We developed a conceptual model of EUCRs and NEUCRs in social networks b. Based 3.5P = 0.2834) in the actual quantified EHR access frequency throughout the study (P = 0.9339) in the actual quantified EHR access frequency throughout the study (P = 0.0171)  at 6 months post-disclosure. Notably, EUCRs in the GRS group were even more likely than EUCRs in the CRS group to seek health information outside of the EHR and share health information in social networks, e.g., with friends or family , supporting the impact of GRS disclosure  (‘Expansion of results’ in Appendix C).There was no difference between CRS participants and GRS participants (37(6) versus 46(6); he study . Similarhe study . However4EHR access by individuals may play a role in health promotion in their non-digital social and health networks. In this secondary analysis of the MI-GENES trial, we hypothesized that individuals who self-reported EHR access to seek their CHD risk information actually accessed the EHR frequently and shared the gleaned health information widely in their social networks. Those who self-reported EHR access to seek their CHD risk information (EUCRs) more frequently accessed the EHR, sought health information online outside of the EHR, shared health information in their social networks, encouraged others to be screened for their CHD risk, and had a wider information sharing radius than individuals who did not log in to review their risk (NEUCRs). Our conceptual model illustrated the potential role of EUCRs in dissemination of health information in social networks.Survey responses were key to understanding reasons for accessing the EHR. For example, two thirds of participants did not correctly recall their disclosed CHD risk. Persistent poor recall may have led to repeated EHR access to retrieve CHD risk information. However, there was no difference in risk recall between EUCRs and NEUCRs, suggesting that poor recall alone does not explain the behavior of EUCRs. The EUCRs accessed their EHR frequently (based on objective quantification) and reported other information exchange behaviors; several individuals accessed their EHR to seek their CHD risk information only after receiving their genetic risk information. With each login, there was potential to again retrieve risk information; perhaps each review episode corresponded with further sharing of the CHD risk with others in the study participants' social network. Notably, information seeking and sharing have both been perceived as health-promotive activities by mediating bidirectional social information acquisition . RegardlImportantly, studies have revealed that a GRS can lead to reclassification of CHD risk estimates , and is We developed a conceptual model of the role of EUCRs and NEUCRs in social networks b  their social networks. Such individuals could be termed ‘central nodes or hubs’ in formal social network analyses. In social networks, individuals are considered as nodes and interpersonal communication and relationships as connections between nodes . CentralStudy limitations include a relatively small (n = 203) and relatively well-educated sample. All participants were whites – the primary population in which the GRS for CHD was developed. Survey responses could reflect an element of recall bias. Nevertheless, this secondary analysis of the MI-GENES study is the first to report that frequency of EHR access can be used to identify individuals who are more likely to share CHD risk information in their social networks and thereby help promote CHD prevention in population health.


Antineutrophil cytoplasmic antibody (ANCA) vasculitis is characterised by autoantibodies against myeloperoxidase (MPO) and proteinase 3 (PR3). The evidence that ANCA are pathogenic comes from in vitro studies in which IgG from patients with anti-MPO or anti-PR3 antibodies activate neutrophils to undergo respiratory burst and degranulation. Furthermore, murine monoclonal antibodies against human MPO and PR3 and a chimeric humanised anti-PR3 monoclonal antibody activate neutrophils. The paradigm of neutrophil activation by ANCA has therefore become established.10.1136/annrheumdis-2018-214405.supp1Supplementary dataWe assessed the effect of purified ANCA on the activation of TNFα primed neutrophils using 10 control IgGs, 11 MPO-ANCA and 9 PR3-ANCA using two different assays of the neutrophil respiratory burst  formation, but this was unaffected by IgG depletion. In addition, purified IgG was unable to induce NET formation. This suggested that factors in the serum of patients with vasculitis, other than IgG, could activate neutrophils. This raises the possibility that the purity of IgG preparations could have influenced results in previous studies. We emphasise that our data do not exclude a role for neutrophils in the pathogenesis of ANCA vasculitis. ANCA may have direct or indirect effects on neutrophils in vivo that are not evident using in vitro assays of activation. We also acknowledge that there are many previous publications suggesting that ANCA do activate neutrophils in vitro and encourage other investigators to re-examine this question.A recent report consistent with our data suggests that ANCA IgG does not activate neutrophils in vitro.


These novel data reflect the effects of smoking and disease on peripheral blood NK cell phenotypes, provide insight into the potential immune pathophysiology of COPD exacerbations, and indicate that NK cell phenotyping may be a useful and biologically relevant marker to predict COPD exacerbations.Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death worldwide. COPD is frequently punctuated by acute exacerbations that are precipitated primarily by infections, which increase both morbidity and mortality and inflates healthcare costs. Despite the significance of exacerbations, little understanding of immune function in COPD exacerbations exists. Natural killer (NK) cells are important effectors of innate and adaptive immune responses to pathogens and NK cell function is altered in smokers and COPD. Using high-dimensional flow cytometry, we phenotyped peripheral blood NK cells from never smokers, smokers, and COPD patients and employed a non-supervised clustering algorithm to define and detect changes in NK cell populations. We identified greater than 1,000 unique NK cell subpopulations across patient groups and describe 13 altered NK populations in patients who experienced prior exacerbations. Based upon cluster sizes and associated fluorescence data, we generated a logistic regression model to predict patients with a history of exacerbations with high sensitivity and specificity. Moreover, highly enriched NK cell subpopulations implicated in the regression model exhibited enhanced effector functions as defined by  Costs related to the treatment of COPD and associated comorbidities are a significant economic burden to patients and health care systems. Acute exacerbation in COPD (AECOPD) is a significant risk factor contributing to increased morbidity and mortality in affected COPD patients. Exacerbations may also result in significant reductions in lung function that is in part due to increased airway and systemic inflammation. Acute worsening of symptoms such as shortness of breath, increased cough, excessive mucus production, and enhanced fatigue often occur during COPD exacerbations. Additionally, an acute exacerbation can dramatically reduce a patient’s quality of life and significantly decrease lung function2. The initiators of exacerbations are still poorly understood but are likely due to inflammatory responses to bacterial or viral infections, or inhalation of environmental particulate matter. Destruction of lung parenchyma and tissue remodeling in COPD is likely due, in part, to pathological alterations in immune cell function3. Recent work has suggested that some patients may be more susceptible to exacerbations than others and represent an ‘exacerbator phenotype’ associated with COPD-dependent changes in systemic immune function4. Despite recent progress in defining the altered immune function in COPD, how smoking and COPD-related changes in immune function contribute to AECOPD remains poorly understood.Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in the United States and worldwide5. NK cells are well described in the context of host-rejection of virally infected cells and tumor surveillance. Surface receptors recognize tumor, virally infected, and stressed cells, as well as many pathogens. The activating  and inhibitory Killer-cell immunoglobulin-like receptors (KIR) receptors and their engagement with targets determine the cytokine and cytolytic response of NK cells. Activated cells release cytokines such as interferon gamma (IFNγ) and tumor necrosis factor alpha (TNFα), among others, which recruit and activate other immune system cells. Perforin release from NK cells causes pore formation in target cell membranes; proteases such as granzyme A and B can enter the cell to induce apoptosis by caspase cleavage as well as cleave other cellular targets. Therefore, aberrant and/or chronic activation of NK cells can result in chronic inflammation and tissue damage.Natural killer cells are lymphocytes that function as part of the innate immune system and are now classified as part of the family of innate lymphoid cells 1 (ILC1)8. These data reveal several changes in the expression of NK cell activating/inhibitory receptors and the size of the NK cell subpopulations associated with smoking and COPD. Furthermore, we demonstrate that logistic regression analysis using population size and receptor expression from peripheral blood NK cells is highly predictive of patients with a previous exacerbation. These findings provide an entry point to more closely examine the effects of smoking on NK cell phenotype and function, the role of NK cells in COPD exacerbations, and the use of NK cells as biomarkers for exacerbations.Given the morbidities and economic burden of COPD and AECOPD, it is important to examine the immune function of these patients in greater detail than previous studies. In this study, we utilized polychromatic flow cytometry to measure the expression of an array of activating and inhibitory receptors on peripheral blood natural killer cells from never smokers, current smokers, and COPD patients. We identified >1,000 significant and unique NK cell subpopulations using the Scalable Weighted Iterative Flow-clustering Technique (SWIFT)9 and the likely influence of environmental factors in the shaping of the NK cell repertoire, we hypothesized that unique NK cell subsets emerge in response to long term smoke exposure and as a consequence of COPD exacerbations. To address this hypothesis, we analyzed NK cells from PBMC samples from 5 patient groups collected at the Cincinnati Veterans Administration Hospital. The patient groups included Never Smokers (NS), Current Smokers (CS), Former Smokers (FS), Gold I/II COPD, and Gold III/IV COPD. Patient demographic and spirometry data are shown in Table 9, we developed a panel of 12 antibodies heavily weighted in activating receptors and activation markers to probe the differences in NK cell populations between these groups , CD335(NKp46), KLRC1, NKG2C, CD314(NKG2D), CD57, CD69, and inhibitory receptors CD158a, CD158b, along with a live/dead dye).Given the recent discovery of a vast array of potential NK cell subpopulations by high resolution analysesin vivo and in vitro, to be associated with alterations to NK surface phenotype and function11. Therefore, patients with an exacerbation and possible ICS use in the month prior to enrollment were excluded. We examined the effects of routine, maintenance dose ICS on surface NK cell receptor expression in the two primary NK cell populations. Figures dimCD16+ or CD56+CD16− NK cells. Representative scatter plots are shown in Fig. The data reveal few significant effects of smoking or COPD status on the mean fluorescence intensity (MFI) across the entire population of NK cells. The expression of CD336 (NKp46), CD314 NKG2D), and CD335 (NKp44) are shown in Fig. , and CD312. Therefore, we employed a non-supervised clustering algorithm to analyze NK cell phenotypes. The SWIFT  algorithm was used to analyze our data as this algorithm preserves important biological subpopulations in data from large high dimensional data sets and is capable of detecting rare subpopulations7. Briefly, SWIFT is a mixture model clustering that first identifies all clusters present within the data by patient group  which generates a “template” cluster description. The “templates” are then combined into a joint model and then clusters identified in individual patient data files. For each cluster present, cells compete for membership in the identified clusters. This process serves to identify subsets of cells that are altered between patient groups. SWIFT clustering analysis identified 1041 cell clusters across the five patient groups. To limit the number of clusters with few cells we took the average cluster size per patient group, then summed across COPD groups and discarded clusters with less than 1000 cells across the 5 patient groups. From these clusters we identified 28 clusters with significant changes by performing Kruskal-Wallis one-way analysis of variance followed by a 10% false discovery rate (FDR) multiple test correction. These clusters were further restricted to 13 clusters that were CD56+ representing NK cells from peripheral blood. These analyses indicate that smokers and COPD patients exhibit unique NK cell populations marked primarily by lower CD56 expression, high CD57 expression (activation and maturity marker), and high expression activation markers NKp46 and NKp44  includes the size of three specific clusters  has an area under the curve (AUC) of 95%  and inhibitory Killer-cell immunoglobulin-like receptors (KIR) receptors can define subsets of NK cells and their engagement with targets ultimately determine the cytokine and cytolytic responses of NK cells. Although CD56/CD16 markers are broadly utilized to identify NK cells, changes in expression levels of specific surface markers can also be used to define specific subsets in health and disease. For example, a unique subset of NK cells that express high levels of CD57 and NKG2C expands during human cytomegalovirus (CMV) infection14. This subpopulation exists across species as a very similar subset of NK cells expands and persists in mice infected with CMV15. Another example of functional division of NK cell subsets is achieved by CD27 and CD11b expression levels to discriminate maturation levels16. Characterizing NK cells from distinct tissues and during infections have also revealed specialized subsets that reflect inherent cellular plasticity in terms of activation and utilization of various signaling pathways18.In humans, two NK cell subsets have been well characterized: CD56+CD56dim population of NK cells14. Functionally, CD57 is associated with cell adhesion and homing to inflamed tissue. Increased CD57 is also associated with cytomegalovirus (CMV), or Epstein-Barr virus (EBV) infection. The reason for the increased CD57 expression in current smokers is unknown. One plausible explanation is the increased inflammatory signaling milieu due to cigarette smoke. We have previously shown that CS exposure primes and enhances NK activation in mice19 and NK cell function is long understood to be altered in smokers20. This suggests that smoking-dependent chronic inflammation may drive the activation and maturation of CD16+CD56dim NK cells due to the inflammatory milieu21. We also observed a decline in CD57 MFI as COPD disease progression increases. This finding seemingly contradicts previous studies by Olloquequi et al. that show increased CD57+ cell density in pulmonary connective tissue and increased density of CD57+ cells in pulmonary lymphoid follicles of late stage COPD patients23. However, our study examines NK cells in the peripheral blood whereas other studies examined CD57+ cell by immunostaining preserved lung tissues without discriminating the cell type expressing CD57. Furthermore, an important physiologically role of CD57 is adhesion and homing. Therefore, our data may be reflective of these observations in lung tissue whereby increased CD57 expression results in increased migration from the periphery into tissues.In addition to providing insight into the population level biology of exacerbations, our analysis provides insight into individual markers used to generate the population identifications. For example, the activation/NK cell memory marker CD57 is expressed at significantly higher levels in smokers, former smokers, and GOLD I/II COPD patients compared to never smokers. CD57 expression is reported to be a marker of mature and ‘memory’ NK cell and is found within the more mature CD1614. However, we see both increased cytokine responsiveness and cytotoxicity in smokers/COPD patients. These findings do not align with the proposed singular functions of these expression levels and suggests we are identifying unique populations of NK cells in the context of COPD.It is important to emphasize that although there are significant changes in surface expression of NK cell receptors, the majority of changes in significant SWIFT NK populations are based on the decreasing size of the population. This infers that the populations are contracting as a consequence of smoking/COPD/exacerbations or these patient groups inherently possess fewer of these NK cell subpopulations. That said, there are several potential outcomes to these studies that we can anticipate based on the data and published literature. One possible outcome is that the identified subpopulations are equally responsive on a per cell basis but the decreased numbers of these cells in exacerbators render the NK cell pool less responsive to challenge. Another possibility is that these subpopulations are functionally repressed which, together with fewer numbers of cells, increases the susceptibility to exacerbation. These would be straightforward conclusions and easily interpreted from the results. However, we hypothesize that the results present a more complex picture of NK cell effector function. Previous studies have demonstrated that there are distinct differences in NK cell subpopulations based on CD56 expression. Moreover, high CD57 expression (which we observe in smokers and COPD patients) is associated with increased cytotoxicity and lower cytokine responsiveness representing a more mature phenotype and demarcate NK cells associated with memory against prior CMV infectionet al. report that budesonide or prednisolone treatment decreased NK cell activity in Crohn’s disease but by 10 weeks post-steroid treatment, NK activity had returned to baseline in the budesonide group and was enhanced in the prednisolone group24. Systemic hydrocortisone treatment results in significant changes in immune function. However, by 7–28 days post steroid treatment cell phenotype and function had largely returned to baseline25. The patients in our study were recruited from a population of VA patients who had at no exacerbations in the month prior to enrollment. Taken together, it is likely that most exacerbators were not currently on ICS at the time of sample collection.Potentially, the changes in observed NK phenotypes may be due to ICS use in COPD patients rather than exacerbations. We examined NK surface phenotype in the study patients in relation to ICS use and did not observe any ICS dependent changes in surface phenotype. Van Ierssel 29. Ideally, biomarkers will better reflect disease activity while representing biologically relevant pathways. Theoretically, biomarkers of COPD exacerbations will identify subgroups of patients who would benefit from specific interventions and conversely avoid the side effects of prolonged exposure to unnecessary drugs. Furthermore, the identification of biomarkers associated with COPD exacerbations will undoubtedly guide future basic research into the mechanisms contributing to the pathogenesis of COPD leading to new therapeutic targets. There are few biological markers that correlate to exacerbation risk or susceptibility except having had a prior exacerbation31. One natural candidate for increased exacerbation risk, or an “exacerbator phenotype”, is the altered immune system of COPD patients. Our data clearly demonstrate there are long lasting alterations to NK cell subpopulations in COPD patients compared to NS and that these changes are associated with prior exacerbations. Thirteen CD3−CD56+ clusters were significantly altered and three of these were strong predictors of a prior exacerbation by logistic regression. Our data indicate that a patient who has had a prior exacerbation subsequently has an identifiable change in natural killer cell phenotype in their peripheral blood. We cannot determine if the changes we observe in NK populations precede or are in response to an exacerbation. These cells may represent a susceptible phenotype of NK cells that preferentially undergo apoptosis in response to an exacerbation32. Alternatively, the decline in cluster size may represent a change in surface phenotype, and therefore cluster membership, that our analysis cannot track. The COPD and smoke-dependent cytokine milieu may shape the development and maintenance of NK cell clusters. Two clusters (9 and 12) had moderate to high CD57 expression indicating that they are composed of mature, terminally differentiated NK cells. The observed differences in cytotoxicity between clusters 9 and 12 compared to bulk NK cells imparts plausible biological relevance as well. This finding suggests that these populations of cells may be more responsive to stimuli associated with exacerbations and consistent with our observations that the sizes of these clusters are decreased in patients with prior exacerbations. Other work has shown that there is an increase in CD57+ cells in COPD lungs. Therefore, the reduction in cluster size is potentially due to migration into tissue from the periphery. The phenotypes of these clusters may be a biomarker of exacerbation susceptibility and/or also represent an ‘exacerbator phenotype’. Taken together, cigarette smoke causes changes in an NK cell phenotype profile that is stable across patients with differing levels of COPD severity stratified by airflow severity.Classification of COPD exacerbation risk is an important metric in the care of COPD patients. Exacerbations increase morbidity and mortality in these patients as well as augment health care costs and resource utilization. As opposed to the subjective and variable physical and physiological endpoints used to guide therapy in stable COPD, there are no reliable indicators of a patient’s exacerbation status, susceptibility, or disease progression. Accordingly, there is a growing interest in the development and use of biomarkers to assess disease and predict disease progression in COPD30. Our analysis is partly limited due to the relatively small sample size. As a result, we were unable to split the data into training and test data sets to validate the logistic models. Additionally, the surface markers we used may not have the depth to completely discriminate populations of NK cells in peripheral blood. While our data show a relation between exacerbation and peripheral blood NK phenotype, we were unable to determine exacerbation cause .Our patient samples only include a sample of whole blood and spirometry taken at the time of study enrollment. Demographic data and the number of exacerbations in the prior two years were recorded at study enrollment. Additionally, our exclusion criteria rejected patients with an exacerbation in the month prior to enrollment. Therefore, these clusters and associated cluster phenotypes are stable for up to two years and the changes in phenotype represent long-term alterations in the immune profile of COPD patients with and without exacerbations. If the NK phenotype is present prior to an exacerbation, perhaps due to cigarette smoke exposure, COPD state, and/or genetic background, then these populations may be biomarkers of exacerbation risk. Alternatively, if these NK phenotypic changes occur in response to or after an exacerbation, they provide a window to study altered immune responses in patients who are now at increased risk of a future exacerbationIn summary, we assessed the expression of an array of activating and inhibitory receptors on peripheral blood NK cells from never smokers, current smokers, and COPD patients using flow cytometry and high-dimensional analyses. These data showed few significant changes in the expression of NK cell receptors within very large classifications of NK cells but reveal several changes in i) the expression of NK cell activating/inhibitory receptors among NK cell subpopulations, and ii) the size of the NK cell subpopulations associated with smoking and COPD. Furthermore, we demonstrate that logistic regression analysis using population size and receptor expression from peripheral blood NK cells is highly predictive of patients with a previous exacerbation. These findings provide an entry point to more closely examine the effects of smoking on NK cell phenotype and function, the role of NK cells in COPD exacerbations, and the use of NK cells as biomarkers for exacerbations. Future separate studies that more comprehensively probe the many effector functions of the various populations of NK cells will help to define these novel biomarkers and immune pathways that can be used therapeutically to manage COPD exacerbations and slow the progression of this disease.33. During the course of this work a new classification system for COPD patients became more widely employed. For consistency, we have not adopted the 2017 guidelines for COPD staging, and retain the Gold I-IV staging based on FEV1% predicted34. All methods were performed in accordance with the relevant guidelines and regulations.Post bronchodilator spirometry was performed on enrolled patients. Whole blood was withdrawn by venipuncture into anti-coagulated lithium heparin tubes and immediately processed to isolate peripheral blood mononuclear cells (PBMC) by Lymphoprep density gradient centrifugation using SepMate-50 tubes (Stemcell Technologies). PBMC were then cryo-preserved in freezing media  in a Mr. Frosty Freezing Container (ThermoFisher) and then stored in liquid nitrogen until use. At the initiation of this study the GOLD I-IV staging was standard of practice at the Veterans Administration hospital in CincinnatiNK cell surface staining was performed on quick-thawed peripheral blood mononuclear cells (PBMC). Antibodies for flow cytometry were obtained from BD Biosciences : BV786 mouse anti-human CD3 (SK7); BUV395 mouse anti-human CD56 (NCAM16.2); BUV737 mouse anti-human CD16 (3G8); R-PE mouse anti-human NKp44/CD336 (p44–8); BV510 mouse anti-human NKp46/CD335 (9E2/NKp46); BV650 mouse anti-human NKG2D/CD314 (1D11); BV711 mouse anti-human CD158a (HP-3E4);. Antibodies for FITC mouse anti-human NKG2C/CD159c (134591) were obtained from Biotechne . Antibodies for R-PE-Cy7 mouse anti-human CD158b (DX27) and mouse anti-human CD57 (HNK-1) were purchased from BioLegend . PerCP-efluor 710 mouse anti-human CD69 (FN50) and LIVE/DEAD Fixable Near-IR Dead Cell stain kit were purchased from ThermoFisher Scientific.− negative cells were then gated and NK cells were discriminated based on CD16++ CD56dim vs. CD16− CD56bright. Appropriate negative controls and fluorescence minus one (FMO) were used to set gates for the above populations. UltraComp eBeads (eBioscience) were used to determine the compensation between fluorophores.Briefly, cells were thawed and washed in ice cold media  labelled with Fixable Near-IR Live/Dead viability dye for 20 minutes on ice and in the dark. Cells were then washed in ice-cold 2X in flow buffer  to remove excess Live/Dead viability dye. Cells were then blocked with Fc Receptor Binding Inhibitor  and mouse γ-globulins  for 1 hour in the dark at 4 °C. Cells were then stained with appropriate antibodies for 1 hour at 4 °C in the dark. Cells were then washed 2X in FB and immediately analyzed by flow cytometry (BD Bioscience). Batches of samples (n = 6–7) were analyzed at a time. All flow cytometric data were acquired using equipment maintained by the Research Flow Cytometry Core in the Division of Rheumatology at Cincinnati Children’s Hospital Medical Center. Samples were analyzed on a 5-laser BD Fortessa 2 equipped with 355 nm (UV), 405 nm (violet), 488 nm (blue), 561 nm (yellow/green) and 640 nm (red) lasers that allow for the detection of 2 scatter parameters and up to 18 fluorescence parameters. Appropriate filters were used to collect data from the fluorophores used in the study. Data was collected with Diva 6.02 and analyzed with FCS Express V5 . Cells were gated first on the lymphocyte population based on forward scatter area (FSC-A) vs. side scatter area (SSC-A) scatter. Next, doublets were excluded by gating (FSC-A vs. FSC-H) on single cells. Subsequently, cells were gated on viable cells. Where necessary CD38. SWIFT 3.0 scripts were used in MATLAB (R2016a) to generate the cluster profiles. Briefly, new concatenated fcs files were created by subsampling the individual patient fcs files within each patient group. The fcs files were first gated on viable and CD3- cells and then used in the SWIFT analysis. The data for each fluorophore was arcSinh transformed and then each consensus patient group fcs files  were run through the main SWIFT clustering algorithm. The resultant cluster template profiles were then combined, and the individual patient fcs files run through the joint template data. The output from this analysis included comma separated values (csv) files with the cluster ID as well as the number of cells in each cluster in each specimen. We limited the clusters we examined in by selecting clusters with at least 1000 cells across treatments, or approximately 1% of the CD56+ cells collected from each patient. SWIFT identified 1041 clusters, and after selection of clusters with >1000 cells, and a 10% FDR we were left with 17 clusters. Of these, 13 clusters were CD56+ NK cells.Scalable Weighted Iterative Flow-Clustering (SWIFT) clustering was used to identify unique peripheral blood phenotypes unique to VA COPD patient groupsFrozen PBMC from three never smokers were quickly thawed and NK cells isolated with anti-CD56 magnetic beads (Miltenyi) according to manufacturer’s instructions. Enriched NK cells were incubated with Alexa Fluor 647 anti-human CD57 (HNK-1), BV650 mouse anti-human NKG2D/CD314 (1D11); and PerCP-efluor 710 mouse anti-human CD69 (FN50) for 20 min, on ice, in the dark. Cells were washed with IMDM with 10% FBS and placed on ice for sorting. Cells were sorted on a MoFlo XDP (Beckman Coulter) at the Cincinnati Children’s Hospital Research Flow Cytometry Core. Cells were sorted into 4 populations that approximate bulk NK cells and implicated clusters: Cluster 12, CD57 + NKG2D + CD69+; Cluster 9, CD57 + NKG2D + CD69-; Cluster 868, CD57-NKG2D + CD69− and bulk NK cells, CD57− NKG2D + CD69-. Cells were placed in IMDM with 10% FBS and incubated with IL-2 (100 U/ml) in a cell culture  incubator for 1 hour. An appropriate quantity of sorted NK cells was combined with 5,000 K562 target cells in round bottom 96-well plates in IMDM with 10% FBS and 100 U/ml IL2. Microwell plates were briefly centrifuged (3 min @ 300 g) to collect cells and then placed in a cell culture incubator for 4 hours. Cells were then removed and placed on ice for analysis by flow cytometry. Propidium Iodide (BioLegend) was added at a final concentration of 5ug/ml. Samples were then analyzed on an Attune Flow Cytometer (Invitrogen) and PI negative and positive K562 cells enumerated. Specific Lysis was determined as; (%PI positive cells - % PI positive background)/(100- % PI positive background).All statistical tests were performed in R (3.3.2) and Rstudio (v1.0.153). Plots were generated using ggplot2 (v2.1). Differences between groups were considered statistically significant when p < 0.05. Analysis of Variance (ANOVA) and post-hoc tests were performed in R. Significant clusters were determined by using the non-parametric Kruskal–Wallis one-way analysis of variance test. Significant clusters with at least 1000 cells across the patient groups were then selected by employing a 10% False Discovery Rate (FDR). Logistic regression analysis employed generalized linear model component in R. Receiver Operator Curves (ROC) (v1.0.7) employed the ROCR package.Study procedures were performed after obtaining written informed consent from patients visiting the Cincinnati Veterans Administration hospital. The study design was reviewed and approved by the VA Research and Development Committee and the University of Cincinnati Institutional Review Board (IRB# 2014–2354). All methods were performed in accordance with the relevant guidelines and regulations. Data from patients, such as questionnaires, spirometry results, and all specimens were assigned a unique study number to de-identify patient personal information.


Following the event of DNA damage, the level of tumour suppressor protein p53 increases inducing either cell cycle arrest or apoptosis. Junctional Mediating and Regulating Y protein (JMY) is a transcription co-factor involved in p53 regulation. In event of DNA damage, JMY levels also upregulate in the nucleus where JMY forms a co-activator complex with p300/CREB-binding protein (p300/CBP), Apoptosis-stimulating protein of p53 (ASPP) and Stress responsive activator of p53 (Strap). This co-activator complex then binds to and increases the ability of p53 to induce transcription of proteins triggering apoptosis but not cell cycle arrest. This then suggests that the increase of JMY levels due to DNA damage putatively “directs” p53 activity toward triggering apoptosis. JMY expression is also linked to increased cell motility as it: (1) downregulates the expression of adhesion molecules of the Cadherin family and (2) induces actin nucleation, making cells less adhesive and more mobile, favouring metastasis. All these characteristics taken together imply that JMY possesses both tumour suppressive and tumour metastasis promoting capabilities. A salient observation in cancer biology has been that TP53 is frequently mutated in many human tumours ,2. p53 pp53 regulation of apoptosis (programed cell death) is at the heart of neoplastic proliferative control. In mammalian cells, apoptosis can be either p53 transcriptional dependent or p53 transcriptional independent. The p53 transcriptional dependent mechanism is set in motion by cellular stress like DNA damage which triggers p53 post translational modification of phosphorylation and acetylation stabilizing p53. Stabilization enables p53 to bind and activate pro-apoptotic genes. It is highly speculated that p53 selectively activates transcription of pro-apoptotic target genes upon interaction with transcriptional co-activators such as p300/CREB-binding protein (p300/CBP), Junctional and regulatory protein (JMY), Stress responsive activator of p53 (Strap) and Apoptosis-stimulating protein of p53 (ASPP) ,5.Some studies have shown that inhibition of mRNA and protein synthesis which inhibit transcription of p53 target genes did not inhibit p53 dependent apoptosis. A plausible explanation for this is the existence of an alternative p53 dependent apoptosis that is transcriptionally independent. According to Bossi et al., studies corroborating this hypothesis show that upon DNA damage, p53 can localize to the mitochondria where it triggers a rapid apoptotic response which occurs even before p53 target genes are activated in the nucleus. This alternate apoptotic response is attained by the p53 DNA binding domain directly binding pro-apoptotic proteins BCL-XL and BCL2 within the mitochondria facilitating the release of BH3 protein and induction of mitochondrial permeabilization and apoptosis .The activity of p53 is tightly controlled and regulated at multiple levels and the importance of co-factors influencing these processes is becoming increasingly evident . UbiquitCBP and p300 are a family of acetyltransferase which act as transcriptional activators for several transcription factors including p53 . FollowiThe Junctional Mediating and regulator Y protein (JMY) was originally identified as a CBP/p300 co-factor regulating p53 activity . Upon DNMetastasis is facilitated by increased cell motility, allowing tumour cells to invade and colonize surrounding as well as distant tissues. In addition to its role in p53 induced apoptosis, JMY also participates in the enhancement of cell motility ,12. JMY This in effect supports the hypothesis, generated by micro array study showing an inverse relationship between JMY and Trap1 : hypoxiaInteresting, Coutts et al. have demonstrated that JMY localizes in the nucleus and cytoplasm but following stress or DNA damage, JMY migrates from the cytoplasm to the nucleus to transcriptionally enhance P53’s response . AccordiJMY was originally identified by Shikama et al.  while usTranscription of the E2F family of proteins can lead to either cell proliferation or apoptosis, hence suggesting a dichotomy in their function . DNA damStrap is another co-factor involved in the p53 regulation through its interaction with both p300/JMY in a co-activation complex. Strap contains six tetratricopeptide (TPR) repeat motifs composed of several protein binding regions which can allow Strap protein to bind different substrates and form multiple complexes ,20,21. FMDM2 ubiquitinates p53 causing its degradation. However, in the event of DNA damage when p53 is phosphorylated, there is minimal interaction between p53 and MDM2 allowing its escape from degradation B 5,9]. . 9]. LevHigher levels of p300/JMY increase p53 transcriptional activity leading to increased apoptosis but not cell cycle arrest . As discP53 activation can lead to both cell cycle arrest and apoptosis, or just one of the two processes. However, the cue for p53 to activate just one of either processes/pathways had remained unknown. As recently unveiled, the path to apoptosis is via JMY and activation of other pro-apoptotic factors . In the Another prominent role of JMY is to regulate motility by affecting actin nucleation and cell adhesion . Actin fSpontaneous assembly and nucleation of actin trimers and dimers are kinetically unfavourable. To counteract this obstacle, cells use actin nucleators and nucleation-promoting factors (NPF) to jump start actin nucleation and filament formation. Actin filament formation via branching is usually facilitated by the actin nucleator “Actin related protein 2/3 complex” (Arp2/3). Arp2/3 is activated by NPFs via their ‘Wiskot-Aldrich Syndrome protein (WASp) homology-2’domain , which are actin binding motifs that enable assembly of actin monomers . AlternaWith the discovery of JMY protein sequence homology to actin regulators and nucleators, came the knowledge of its involvement with actin . JMY’s cZuchero et al.  and FiraWe previously discussed how JMY accumulates in the nucleus in response to DNA damage. However, another mechanism regulating its subcellular localization has been unveiled, this time in response to polymerization of actin monomers which ordinarily would bind to the JMY’s WH2 domains inhibiting JMY’s transfer to the nucleus . This hyThe binding of an actin monomer to JMY can prevent JMY’s transfer to the nucleus, because it competes with Importin’s binding to JMY, since the Actin binding region overlaps with the Nuclear Localization Signal region targeted by Importin . ImportiThe second mechanism by which JMY affects motility is via its regulation of adhesion molecules E-Cadherin and N-Cadherin levels . We haveIt has become increasingly evident that JMY plays prominent roles in coordinating DNA damage response and cell motility. Considering JMY’s association with p53, this also implies that p53 plays a role in inhibition of cell motility  34]. p5. p534]. JMY’s capacity to regulate cell motility and DNA damage response suggest that JMY could be the link between these two cellular processes. In the model, currently being explored (illustrated in Having the ability to promote p53 induced apoptosis on one hand, and facilitate invasiveness of the cancer cells on the other, JMY is an example of genes that can act both as a suppressor gene and as a gene promoting tumour progression . We have


Anaplastic meningioma is a World Health Organization (WHO) Class III lesion representing 2–3% of all meningiomas, with more aggressive spread, increased mortality and increased likelihood of recurrence. Metastases outside the blood–brain barrier are uncommon but can occur to the lungs, liver, bone and skin.Definitive diagnosis is obtained with pathological analysis. The World Health Organization classifies meningiomas into benign (Grade I), atypical (Grade II) and anaplastic/malignant (Grade III) based on histological findings including number of mitoses, cellularity, nucleus to cytoplasm etc. This case presents a 58-year-old female with history of treated anaplastic meningioma with new onset headache, nausea and vomiting. Workup demonstrated multiple new bilateral pulmonary nodules, which subsequent biopsy proved to be metastasis from recurrent anaplastic meningioma, with extensive intrathoracic involvement. The patient is a 58-year-old female with past medical history of left breast cancer status post resection and chemotherapy (2000), basal cell carcinoma of the forehead (1997) and pathology proven anaplastic intracranial meningioma (9/2012) status post three surgical resections, radiation therapy and radiosurgery with ongoing antiangiogenic therapy with bevacizumab, who presented to the emergency department in mid-2014 with headache, nausea and vomiting. Acute abdominal series demonstrated multiple new bilateral pulmonary nodules. Subsequent CT revealed both pulmonary and pleural components, and MRI of the brain demonstrated recurrent brain mass in area of previous surgical resection . Whole b1 They are often found incidentally and can present with symptoms relating to size and location such as headache, nausea, vomiting, seizure, visual deficits, hearing loss or weakness. Anaplastic meningiomas represent 2–3% of all meningiomas and demonstrate more aggressive characteristics with increased mortality and likelihood of recurrence. Metastases outside the blood–brain barrier are uncommon but can occur to the lungs, liver, bone and skin.2 Atypical and anaplastic meningiomas have been suggested in some studies to represent a malignant progression of benign meningiomas, with some series suggesting up to 28.5% of recurrent benign meningiomas demonstrating atypical or anaplastic pathology.3Meningiomas arise from arachnoid cap cells of the meninges and represent approximately 13–26% of intracranial tumours.2 Benign meningiomas are more prevalent in females, whereas atypical and anaplastic meningiomas are more commonly seen in men.2 There are some reports of atypical meningiomas after cranial irradiation in younger populations with intracranial tumours as well as implication of dental x-rays or past low-dose experimental treatments for issues such as tinea capitas.4 Radiation-induced meningiomas are more likely to involve multiple sites, to recur early after excision and have a higher tendency to involve osseous structures.5The annual prevalence rate for meningiomas is approximately 6 per 100,000. The majority of meningiomas occurs in middle aged or elderly patients but can also be seen in younger populations with syndromes such as neurofibromatosis Type 2.6 Factors such as male sex, age less than 40 years and meningioma location at the skull base or parasagittal-falcine area are believed to be associated with increased recurrence rate after subtotal resection in anaplastic meningiomas.7 Prognosis is poor after development of recurrent disease as there is high likelihood of treatment failure.4 Median survival time for benign meningiomas with brain invasion is between 10 and 14 years with a 5 year mortality rate of 25%. Median survival time for anaplastic lesions without brain invasion is 1.5 years with a 5-year mortality rate of 68%, but median survival time is reduced to 1.4 years with a 5-year mortality rate of 83% in the presence of brain invasion.8A tumour suppressor gene on chromosome 22q12 has been implicated in the initiation of meningioma formation and is seen in up to 70% of surgical specimens.T1 weighted images or post-contrast T1 enhancement pattern.2 A future role for apparent diffusion coefficient (ADC) mapping and diffusion tensor imaging may be considered in non-invasive differentiation between benign and atypical and anaplastic meningioma with studies showing significantly higher ADC values and more disorganized water motion in benign meningiomas. Other studies suggest a role of magnetic resonance spectroscopy in differentiating higher grade meningiomas by their increases in choline/creatinine ratio, lactate and methylene.10CT characteristics such as heterogeneous precontrast examination with homogenous bright enhancement after contrast injection, associated bone destruction, lack of calcification, indistinct margins and nodular contours are not unique to more aggressive variants and may also be seen in benign meningiomas. Similar remarks can be made about MRI findings such as indistinct margins on 11 The World Health Organization (WHO) classifies meningiomas into benign (Grade I), atypical (Grade II) and anaplastic/malignant (Grade III) based on histological findings including number of mitoses, cellularity, nucleus-to-cytoplasm ratio etc. Criteria for anaplastic meningioma includes “>/=mitotic figures/10 HPF or focal or diffuse loss of meningothelial differentiation resulting in carcinoma-, sarcoma-, or melanoma –like appearance”.12Anaplastic meningiomas are thought to be formed either de novo or via transformation of a pre-existing meningioma.13 Decreased EMA positivity, as seen with the presented case, can occur with recurrence or malignant progression to Grade III anaplastic meningioma.11 S-100 immunoreactivity is not universally seen with meningiomas, as with our case, but when present, is often less reactive than would be expected when sampling schwannoma or solitary fibrous tumour.13 Ki-67/MIB-1 is used as an indication of cellular proliferation, and was found to be up to 45% in the more active areas of the presented case specimen. Ki-67-MIB-1 proliferative index has been found to increase from Grade I to Grade III meningiomas in a large retrospective study performed by Roser et al,14 with mean values of 3.88%, 9.95% and 12.18%, respectively.Immunohistochemical findings that can help differentiate meningioma from other central nervous system neoplasms such as hemangiopericytoma and schwannoma include positive EMA, weakly positive S-100 staining and strong vimentin staining, which often stays positive through the various WHO grades.8 found microscopic brain invasion to be the most powerful predictive tool for disease recurrence. Independently, maximal mitotic rate of >/=4/10 HPF was found to increase the chance of recurrence. Frank histologic anaplasia, defined by the Mayo Clinic scheme as >/=20 mitotic figures/10 HPF or loss of meningothelial differentiation with or without brain invasion, is noted as the worst prognostic finding.Studies by Perry et al8 Anaplastic tumours are often found to adhere to cortical vessels, making resection difficult and increasing the risk of postoperative complications . Hypervascular tumours not supplied by the internal carotid artery may be treated with embolization prior to surgical resection to shrink tumour volume and potentially lower blood loss.15 Repeat surgical resection is commonly performed with disease recurrence. Early postsurgical adjunct therapy with conventional radiation has been shown to slow or halt disease progression, increase disease-free survival and increase overall survival in atypical and anaplastic meningiomas.1 Stereotactic radiosurgery is considered for lesions <3 cm following subtotal resection, recurrent disease, asymptomatic disease or in otherwise non-operative cases.16 Newer antiangiogenic therapies targeting vascular endothelial growth factor and receptors take advantage of a 10-fold increased expression of vascular endothelial growth factor in anaplastic meningiomas, with reports suggesting some delay in disease progression.17 Hydroxyurea has been shown to inhibit in vitro meningioma cell growth, with some response in benign meningiomas but equivocal response in anaplastic tumours. Therapies targeting the platelet-derived growth factor receptors are currently being investigated as these receptors are significantly increased in atypical and anaplastic meningiomas.8Surgical resection is the primary treatment for all grades of meningioma, with complete excision associated with decreased recurrence and increased survival.10 The case presented demonstrates a rare complication of anaplastic meningioma with extracranial metastatic disease. Extracranial metastasis most commonly spreads to the lungs, but can also be seen in the liver, bone and skin.2Anaplastic meningioma is a WHO Class III lesion with worse prognosis and increased recurrence rate after treatment as compared to Class I and Class II lesions. Definitive diagnosis is obtained with pathological analysis with ADC and diffusion tensor imaging characteristics on MRI showing promise in non-invasive differentiation.2Anaplastic meningioma is a WHO Class III lesion representing 2–3% of all meningiomas, with more aggressive spread, increased mortality and increased likelihood of recurrence.2Metastases outside the blood–brain barrier are uncommon but can occur to the lungs, liver, bone and skin.2CT characteristics such as heterogeneous precontrast examination with homogeneous bright enhancement after contrast injection, associated bone destruction, lack of calcification, indistinct margins and nodular contours, are not unique to more aggressive variants and may also be seen in benign meningiomas.12Pathological criteria for anaplastic meningioma includes “>/=mitotic figures/10 HPF or focal or diffuse loss of meningothelial differentiation resulting in carcinoma-, sarcoma-, or melanoma –like appearance”.8Surgical resection is the primary treatment for all grades of meningioma with adjunct therapy with conventional radiation shown to slow or halt disease progression, increase disease-free survival and increase overall survival in atypical and anaplastic meningiomas.Written informed consent for the case to be published  was obtained from the patient(s) for publication of this case report, including accompanying images.


TSPO gene influences the binding affinity of endogenous and synthetic TSPO ligands by facilitating a lower-affinity conformation, which modifies a potential ligand binding site, ultimately leading to a binding profile classification according to each genotype. For instance, some clinical effects of the distinctive binding affinity profile of cholesterol toward the TSPO of individuals with different genotypes have been extensively discussed. Therefore, we conducted an investigation based on a radioligand binding assay, to determine the inhibition constants of some reported endogenous TSPO ligands (diazepam binding inhibitor and protoporphyrin IX), as well as synthetic ligands (disulfiram and derivatives). We observed no dependency of the polymorphism on the binding affinity of the evaluated endogenous ligands, whereas a high dependency on the binding affinity of the tested synthetic ligands was evident. An intriguing target involved in several pathophysiological processes is the 18 kDa translocator protein (TSPO), of which exact functions remained elusive until now. A single nucleotide polymorphism in the  TSPO gene (rs6971 polymorphism), which replaces alanine by threonine (Ala147Thr) in a non-conservative way, is known to influence the binding affinity of some TSPO ligands (endogenous and synthetic) according to the exhibited genotype. In detail, the binding affinities of such ligands vary among individuals with different genotype. Three affinity patterns related to this SNP can be distinguished as follows: High-affinity-binders , mixed-affinity-binders , and low-affinity-binders  [64Cu]Cu-DDC} and proposed as a TSPO PET ligand without any information regarding its sensitivity towards the rs6971 polymorphism PK11195 (a known TSPO ligand (Kd 29.25 nM), which binds to TSPO independent of the rs6971 polymorphism) in platelets membranes derived from subjects genotyped as HAB, MAB and LAB according to a well described assay Cu-DDC as a potential TSPO PET tracer, for which a very high affinity (nanomolar range) is normally required [64Cu]CuCl2 separately) PK11195  solution. Non-specific binding was determined using 20 µM PK11195 instead of the tested substance, whereas total binding was assessed by incubating only [3H]PK11195 and membrane suspension. Incubations were carried out for 1 h at 37 °C. After this time, binding was terminated with ice cold wash buffer , while filtrating the membrane bound through GF/B glass fiber filters  presoaked in assay buffer containing 0.05% polyethylene imine. In addition, 3 mL of wash buffer were passed through the filters and these were transferred into β-counting vials. Finally, 4 mL of β-scintillation cocktail  were added and the radioactivity was counted . Data from the competition plots were analyzed and IC50 and Ki values were calculated using a dissociation constant of 29.25 nM for [3H]PK11195 and GraphPad Prism® software .Following the procedure described by Owen et al. , the radIn summary, this study highlights once again the influence of the rs6971 single nucleotide polymorphism on the binding affinity of endogenous and synthetic TSPO ligands. Although some advances have been made regarding the causes of the poor binding affinity of some ligands in LAB, there is no clear line of evidence in respect of the molecular requirements for homogenous binding affinity toward TSPO. Hence, in vitro evaluation of binding affinities seems necessary to achieve a comprehensive overview of the TSPO as well as for the development of new TSPO ligands.