Leukocyte-mediated degradation of lung extracellular matrix & serum molecules in chronic inflammatory disease, as discerned through urinary biomarkers

Chronic Obstructive Pulmonary disease (COPD) is an irreversible inflammatory disease of the lung, characterised by abnormal inflammation of the lungs in response to inhalation of noxious particles or toxic gases, especially cigarette smoke. COPD exacerbations, defined as acute sustained worsening of symptoms from usual stable state, accounts for significant morbidity and mortality. Improved diagnostics which give advanced warning of an exacerbation could help prevent further declines in lung function. The quest to identify a marker or a combination of markers associated with COPD exacerbations has been pursued for some time. Many groups have studied biomarkers in plasma, serum, sputum and bronchoalveolar lavage (BAL) fluid and uncovered useful markers for prediction of exacerbations, disease severity and mortality. However, there is limited research on urine biomarkers. Profiling inflammatory mediators in urine samples presents a simple, convenient, non-invasive measure of inflammation in COPD patients and can be done repeatedly within their own home or in the clinic, allowing easier monitoring of time-dependent changes in biomarker levels. The research described in this thesis is the first investigation where a large panel of biomarkers has been evaluated in urine samples from subjects in various stages of COPD. This has provided new insights into the relevance and origin of the biomarkers. Prototype point-of-care tests were developed that could be used routinely by patients in their own homes to monitor their inflammation status and predict pulmonary exacerbations. This was evaluated in a prospective observational study, results of which were used to develop a simple algorithm that showed the potential for differentiating between stable state and exacerbation events. The research described here is part of a major research initiative carried out within the Mologic R&D group and constitutes investigations designed and directed by the author, and conclusions derived from the author’s analysis of the data collected by the biomarker immunoassays. The findings constitute a key scientific foundation for a new approach to personalised medicine for COPD sufferers

Characteristics of alpha-1 antitrypsin deficiency related lung disease exacerbations using a daily symptom diary and urinary biomarkers

Background: Pulmonary exacerbations in alpha-1 antitrypsin deficiency (AATD) related lung disease are a significant contributor to disease burden, as with usual COPD. Separating the early stages of an exacerbation from the day-to-day variation in stable COPD is central to the concerns of both clinicians and patients and has been identified as a research priority by NIHR.Clinical tools that distinguish baseline symptoms from those of an exacerbation could allow early and appropriate treatment of AECOPD to reduce the impact and potentially may slow disease progression thereby improving survival and quality of life. Candidate tools include symptom diaries and biomarkers of infection and acute inflammation. Urinary biomarkers of AECOPD have yet to be explored in AATD related COPD. Methods: 55 patients with AATD related lung disease with a history of 2 or more AECOPD in the preceding year were prospectively followed for 18 months. Each patient recorded symptom scores daily via an electronic symptom diary (eDiary) based on Bronkotest. Urinary biomarkers for AAT, NE, CRP, TIMP1 and desmosine were measured weekly using a home urinary lateral flow device. During self-reported AECOPD patients were asked to perform urine analysis on the first 7 consecutive days.Results: Type I Anthonisen exacerbations and episodes occurring in autumn/winter lasted longer than Type II/III exacerbations and spring/summer episodes respectively. Median urinary CRP concentration across all study participants increased during Type I AECOPD. eDiary adherence was 68% over a median of 17.8 months (IQR 15.7 to 18.5).Conclusions: Use of an eDiary and urinary biomarkers to detect and characterise AECOPD remotely in AATD related lung disease is feasible over a prolonged period and paves the way for precision detection of exacerbations. <br/

Measurement of innate immune response biomarkers in peritoneal dialysis effluent using a rapid diagnostic point-of-care device as a diagnostic indicator of peritonitis

Peritonitis is the commonest complication of peritoneal dialysis and a major reason for treatment failure. Current diagnosis is based on clinical symptoms, cloudy effluent and a dialysate white cell count (over 100 cells/μl). A rapid point-of-care diagnostic test would accelerate diagnosis and potentially improve outcomes from infection. Here, in a clinical audit project, we used PERiPLEX®, a point-of-care device which detects when levels of matrix metalloproteinase-8 and interleukin-6 are elevated above a threshold within minutes in dialysis effluent, to assess whether it could confirm or exclude peritonitis in 107 patients undergoing peritoneal dialysis. Mean patient age was 64.6 years with a median duration of peritoneal dialysis of 3.5 months (interquartile range 6.4 – 31.5 months). Presence of peritonitis was confirmed by clinical criteria. There were 49 positive tests of which 41 patients had peritonitis, three had other causes of intra-peritoneal inflammation, three had severe urosepsis and two patients required no treatment. Fifty eight tests were negative with one patient having a false negative result. The positive predictive value of the test was 83.7% (95% confidence interval 72.8 – 90.8) and the negative predictive value was 98.3% (89.1 – 99.8). Sensitivity and specificity were 97.6% (87.4 – 99.9) and 87.7% (77.2 – 94.5) respectively. Thus, PERiPLEX® could be used as a rapid point-of-care test that can aid the diagnosis or exclusion of peritonitis with a high negative predictive value

Control of neutrophil influx during peritonitis by transcriptional cross‐regulation of chemokine CXCL1 by IL‐17 and IFN‐γ

Neutrophil infiltration is a hallmark of peritoneal inflammation, but mechanisms regulating neutrophil recruitment in patients with peritoneal dialysis (PD)-related peritonitis are not fully defined. We examined 104 samples of PD effluent collected during acute peritonitis for correspondence between a broad range of soluble parameters and neutrophil counts. We observed an association between peritoneal IL-17 and neutrophil levels. This relationship was evident in effluent samples with low but not high IFN-γ levels, suggesting a differential effect of IFN-γ concentration on neutrophil infiltration. Surprisingly, there was no association of neutrophil numbers with the level of CXCL1, a key IL-17-induced neutrophil chemoattractant. We investigated therefore the production of CXCL1 by human peritoneal mesothelial cells (HPMCs) under in vitro conditions mimicking clinical peritonitis. Stimulation of HPMCs with IL-17 increased CXCL1 production through induction of transcription factor SP1 and activation of the SP1-binding region of the CXCL1 promoter. These effects were amplified by TNFα. In contrast, IFN-γ dose-dependently suppressed IL-17-induced SP1 activation and CXCL1 production through a transcriptional mechanism involving STAT1. The SP1-mediated induction of CXCL1 was also observed in HPMCs exposed to PD effluent collected during peritonitis and containing IL-17 and TNFα, but not IFN-γ. Supplementation of the effluent with IFN-γ led to a dose-dependent activation of STAT1 and a resultant inhibition of SP1-induced CXCL1 expression. Transmesothelial migration of neutrophils in vitro increased upon stimulation of HPMCs with IL-17 and was reduced by IFN-γ. In addition, HPMCs were capable of binding CXCL1 at their apical cell surface. These observations indicate that changes in relative peritoneal concentrations of IL-17 and IFN-γ can differently engage SP1–STAT1, impacting on mesothelial cell transcription of CXCL1, whose release and binding to HPMC surface may determine optimal neutrophil recruitment and retention during peritonitis

Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms

Women with uncomplicated urinary tract infection (UTI) symptoms are commonly treated with empirical antibiotics, resulting in overuse of antibiotics, which promotes antimicrobial resistance. Available diagnostic tools are either not cost-effective or diagnostically sub-optimal. Here, we identified clinical and urinary immunological predictors for UTI diagnosis. We explored 17 clinical and 42 immunological potential predictors for bacterial culture among women with uncomplicated UTI symptoms using random forest or support vector machine coupled with recursive feature elimination. Urine cloudiness was the best performing clinical predictor to rule out (negative likelihood ratio [LR−] = 0.4) and rule in (LR+ = 2.6) UTI. Using a more discriminatory scale to assess cloudiness (turbidity) increased the accuracy of UTI prediction further (LR+ = 4.4). Urinary levels of MMP9, NGAL, CXCL8 and IL-1β together had a higher LR+ (6.1) and similar LR− (0.4), compared to cloudiness. Varying the bacterial count thresholds for urine culture positivity did not alter best clinical predictor selection, but did affect the number of immunological predictors required for reaching an optimal prediction. We conclude that urine cloudiness is particularly helpful in ruling out negative UTI cases. The identified urinary biomarkers could be used to develop a point of care test for UTI but require further validation

Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections

The immune system has evolved to sense invading pathogens, control infection, and restore tissue integrity. Despite symptomatic variability in patients, unequivocal evidence that an individual's immune system distinguishes between different organisms and mounts an appropriate response is lacking. We here used a systematic approach to characterize responses to microbiologically well-defined infection in a total of 83 peritoneal dialysis patients on the day of presentation with acute peritonitis. A broad range of cellular and soluble parameters was determined in peritoneal effluents, covering the majority of local immune cells, inflammatory and regulatory cytokines and chemokines as well as tissue damage–related factors. Our analyses, utilizing machine-learning algorithms, demonstrate that different groups of bacteria induce qualitatively distinct local immune fingerprints, with specific biomarker signatures associated with Gram-negative and Gram-positive organisms, and with culture-negative episodes of unclear etiology. Even more, within the Gram-positive group, unique immune biomarker combinations identified streptococcal and non-streptococcal species including coagulase-negative Staphylococcus spp. These findings have diagnostic and prognostic implications by informing patient management and treatment choice at the point of care. Thus, our data establish the power of non-linear mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific immune responses

Bronkotest score during exacerbations in different seasons of the year.

Bronkotest score during exacerbations in different seasons of the year.</p

Patient characteristics.

BackgroundPulmonary exacerbations in alpha-1 antitrypsin deficiency (AATD) related lung disease are a significant contributor to disease burden, as with usual COPD. Separating the early stages of an exacerbation from the day-to-day variation in stable COPD is central to the concerns of both clinicians and patients and has been identified as a research priority by NIHR. Clinical tools that distinguish baseline symptoms from those of an exacerbation could allow early and appropriate treatment of AECOPD to reduce the impact and potentially may slow disease progression thereby improving survival and quality of life. Candidate tools include symptom diaries and biomarkers of infection and acute inflammation. Urinary biomarkers of AECOPD have yet to be explored in AATD related COPD.Methods55 patients with AATD related lung disease with a history of 2 or more AECOPD in the preceding year were prospectively followed for 18 months. Each patient recorded symptom scores daily via an electronic symptom diary (eDiary) based on Bronkotest. Urinary biomarkers for AAT, NE, CRP, TIMP1 and desmosine were measured weekly using a home urinary lateral flow device. During self-reported AECOPD patients were asked to perform urine analysis on the first 7 consecutive days.ResultsType I Anthonisen exacerbations and episodes occurring in autumn/winter lasted longer than Type II/III exacerbations and spring/summer episodes respectively. Median urinary CRP concentration across all study participants increased during Type I AECOPD. eDiary adherence was 68% over a median of 17.8 months (IQR 15.7 to 18.5).ConclusionsUse of an eDiary and urinary biomarkers to detect and characterise AECOPD remotely in AATD related lung disease is feasible over a prolonged period and paves the way for precision detection of exacerbations.</div