Impact of Comorbidities on Mortality in Patients with Idiopathic Pulmonary Fibrosis

Introduction

Comorbidities significantly influence the clinical course of idiopathic pulmonary fibrosis (IPF). However, their prognostic impact is not fully understood. We therefore aimed to determine the impact of comorbidities, as individual and as whole, on survival in IPF.

Methods

The database of a tertiary referral centre for interstitial lung diseases was reviewed for comorbidities, their treatments, their frequency and survival in IPF patients.

Results

272 patients were identified of which 12% had no, 58% 1–3 and 30% 4–7 comorbidities, mainly cardiovascular, pulmonary and oncologic comorbidities. Median survival according to the frequency of comorbidities differed significantly with 66 months for patients without comorbidities, 48 months when 1–3 comorbidities were reported and 35 months when 4–7 comorbidities were prevalent (p = 0.004). A multivariate Cox proportional hazard analyses identified other cardiac diseases and lung cancer as significant predictors of death, gastro-oesophageal reflux disease (GERD) and diastolic dysfunction had a significant positive impact on survival. A significant impact of comorbidities associated therapies on survival was not discovered. This included the use of proton pump inhibitors at baseline, which was not associated with a survival benefit (p = 0.718). We also established a predictive tool for highly prevalent comorbidities, termed IPF comorbidome which demonstrates a new relationship of IPF and comorbidities.

Conclusion

Comorbidities are frequent in IPF patients. Some comorbidities, especially lung cancer, mainly influence survival in IPF, while others such as GERD may inherit a more favourable effect. Moreover, their cumulative incidence impacts survival.     

Asymmetric and Symmetric Dimethylarginine as Risk Markers for Total Mortality and Cardiovascular Outcomes: A Systematic Review and Meta-Analysis of Prospective Studies

BackgroundA growing number of studies linked elevated concentrations of circulating asymmetric (ADMA) and symmetric (SDMA) dimethylarginine to mortality and cardiovascular disease (CVD) events. To summarize the evidence, we conducted a systematic review and quantified associations of ADMA and SDMA with the risks of all-cause mortality and incident CVD in meta-analyses accounting for different populations and methodological approaches of the studies.MethodsRelevant studies were identified in PubMed until February 2015. We used random effect models to obtain summary relative risks (RR) and 95% confidence intervals (95%CIs), comparing top versus bottom tertiles. Dose-response relations were assessed by restricted cubic spline regression models and potential non-linearity was evaluated using a likelihood ratio test. Heterogeneity between subgroups was assessed by meta-regression analysis.ResultsFor ADMA, 34 studies (total n = 32,428) investigating associations with all-cause mortality (events = 5,035) and 30 studies (total n = 30,624) investigating the association with incident CVD (events = 3,396) were included. The summary RRs (95%CI) for all-cause mortality were 1.52 (1.37–1.68) and for CVD 1.33 (1.22–1.45), comparing high versus low ADMA concentrations. Slight differences were observed across study populations and methodological approaches, with the strongest association of ADMA being reported with all-cause mortality in critically ill patients. For SDMA, 17 studies (total n = 18,163) were included for all-cause mortality (events = 2,903), and 13 studies (total n = 16,807) for CVD (events = 1,534). High vs. low levels of SDMA, were associated with increased risk of all-cause mortality [summary RR (95%CI): 1.31 (1.18–1.46)] and CVD [summary RR (95%CI): 1.36 (1.10–1.68) Strongest associations were observed in general population samples.ConclusionsThe dimethylarginines ADMA and SDMA are independent risk markers for all-cause mortality and CVD across different populations and methodological approaches.     

Hydrology and Diatom Phytoplankton of High Arctic Lakes and Ponds on Store Koldewey,Northeast Greenland

We document hydrological and phytoplankton characteristics of nine lakes and two ponds on Store Koldewey, a culturally undisturbed island off Northeast Greenland. The limnological survey included the recording of temperature, conductivity, oxygen concentration and saturation, pH, ionic composition, transparency, and the diatom phytoplankton community. In summer 2003, the lakes were cold, monomictic, thermally unstratified, alkaline and likely oligotrophic water bodies. Diatom phytoplankton was present in six lakes and consisted of four dominant species (Aulacoseira tethera, Cyclotella pseudostelligera, C. rossii, and Fragilaria tenera). The concentration of planktonic diatoms varied distinctly between the lakes. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Archazolid A binds to the equatorial region of the c-ring of the vacuolar H+-ATPase

The macrolactone archazolid is a novel, highly specific V-ATPase inhibitor with an IC(50) value in the low nanomolar range. The binding site of archazolid is presumed to overlap with the binding site of the established plecomacrolide V-ATPase inhibitors bafilomycin and concanamycin in subunit c of the membrane-integral V(O) complex. Using a semi-synthetic derivative of archazolid for photoaffinity labeling of the V(1)V(O) holoenzyme we confirmed binding of archazolid to the V(O) subunit c. For the plecomacrolide binding site a model has been published based on mutagenesis studies of the c subunit of Neurospora crassa, revealing 11 amino acids that are part of the binding pocket at the interface of two adjacent c subunits (Bowman, B. J., McCall, M. E., Baertsch, R., and Bowman, E. J. (2006) J. Biol. Chem. 281, 31885-31893). To investigate the contribution of these amino acids to the binding of archazolid, we established in Saccharomyces cerevisiae mutations that in N. crassa had changed the IC(50) value for bafilomycin 10-fold or more and showed that out of the amino acids forming the plecomacrolide binding pocket only one amino acid (tyrosine 142) contributes to the binding of archazolid. Using a fluorescent derivative of N,N'-dicyclohexylcarbodiimide, we found that the binding site for archazolid comprises the essential glutamate within helix 4 of subunit c. In conclusion the archazolid binding site resides within the equatorial region of the V(O) rotor subunit c. This hypothesis was supported by an additional subset of mutations within helix 4 that revealed that leucine 144 plays a role in archazolid binding.     

DNA damage response curtails detrimental replication stress and chromosomal instability induced by the dietary carcinogen PhIP

PhIP is an abundant heterocyclic aromatic amine (HCA) and important dietary carcinogen. Following metabolic activation, PhIP causes bulky DNA lesions at the C8-position of guanine. Although C8-PhIP-dG adducts are mutagenic, their interference with the DNA replication machinery and the elicited DNA damage response (DDR) have not yet been studied. Here, we analyzed PhIP-triggered replicative stress and elucidated the role of the apical DDR kinases ATR, ATM and DNA-PK_cs in the cellular defense response. First, we demonstrate that PhIP induced C8-PhIP-dG adducts and DNA strand breaks. This stimulated ATR-CHK1 signaling, phosphorylation of histone 2AX and the formation of RPA foci. In proliferating cells, PhIP treatment increased the frequency of stalled replication forks and reduced fork speed. Inhibition of ATR in the presence of PhIP-induced DNA damage strongly promoted the formation of DNA double-strand breaks, activation of the ATM-CHK2 pathway and hyperphosphorylation of RPA. The abrogation of ATR signaling potentiated the cell death response and enhanced chromosomal aberrations after PhIP treatment, while ATM and DNA-PK inhibition had only marginal effects. These results strongly support the notion that ATR plays a key role in the defense against cancer formation induced by PhIP and related HCAs.     

Saturating light and not increased carbon dioxide under ocean acidification drives photosynthesis and growth in Ulva rigida (Chlorophyta)

Carbon physiology of a genetically identified Ulva rigida was investigated under different CO_2(aq) and light levels. The study was designed to answer whether (1) light or exogenous inorganic carbon (Ci) pool is driving growth; and (2) elevated CO_2(aq) concentration under ocean acidification (OA) will downregulate CA_ext-mediated dehydration and alter the stable carbon isotope (δ¹³C) signatures toward more CO₂ use to support higher growth rate. At pH_T 9.0 where CO_2(aq) is <1 μmol L⁻¹, inhibition of the known use mechanisms, that is, direct uptake through the AE port and CA_ext-mediated dehydration decreased net photosynthesis (NPS) by only 56–83%, leaving the carbon uptake mechanism for the remaining 17–44% of the NPS unaccounted. An in silico search for carbon-concentrating mechanism elements in expressed sequence tag libraries of Ulva found putative light-dependent transporters to which the remaining NPS can be attributed. The shift in δ¹³C signatures from –22‰ toward –10‰ under saturating light but not under elevated CO_2(aq) suggest preference and substantial use to support photosynthesis and growth. U. rigida is Ci saturated, and growth was primarily controlled by light. Therefore, increased levels of CO_2(aq) predicted for the future will not, in isolation, stimulate Ulva blooms.     

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variance-stabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variance-stabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry.Different techniques are being used and developed in the field of proteomics to allow quantitative comparison of samples between one state and another. These can be divided into gel- (1–4) or mass spectrometry-based (5–8) techniques. Comparative studies have found that each technique has strengths and weaknesses and plays a complementary role in proteomics (9, 10). There is significant interest in stable isotope labeling strategies of proteins or peptides as with every measurement there is the potential to use an internal reference allowing relative quantitation comparison, which significantly increases sensitivity of detection of change in abundance. Isobaric labeling techniques such as tandem mass tags (11, 12) or isobaric tags for relative or absolute quantitation (iTRAQ)¹ (13, 14) allow multiplexing of four, six and eight separately labeled samples within one experiment. In contrast to most other quantitative proteomics methods where precursor ion intensities are measured, here the measurement and ensuing quantitation of iTRAQ reporter ions occurs after fragmentation of the precursor ion. Differentially labeled peptides are selected in MS as a single mass precursor ion as the size difference of the tags is equalized by a balance group. The reporter ions are only liberated in MS/MS after the reporter ion and balance groups fragment from the labeled peptides during CID. iTRAQ has been applied to a wide range of biological applications from bacteria under nitrate stress (15) to mouse models of cerebellar dysfunction (16).For the majority of MS-based quantitation methods (including MS/MS-based methods like iTRAQ), the measurements are made at the peptide level and then combined to compute a summarized value for the protein from which they arose. An advantage is that the protein can be identified and quantified from data of multiple peptides often with multiple values per distinct peptide, thereby enhancing confidence in both identity and the abundance. However, the question arises of how to summarize the peptide readings to obtain an estimate of the protein ratio. This will involve some sort of averaging, and we need to consider the distribution of the data, in particular the following three aspects. (i) Are the data centered around a single mode (which would be related to the true protein quantitation), or are there phenomena that make them multimodal? (ii) Are the data approximately symmetric (non-skewed) around the mode? (iii) Are there outliers? In the case of multimodality, it is recommended that an effort be made to separate the various phenomena into their separate variables and to dissect the multimodality. Li et al. (17) developed ASAP ratio for ICAT data that includes a complex data combination strategy. Peptide abundance ratios are calculated by combining data from multiple fractions across MS runs and then averaging across peptides to give an abundance ratio for each parent protein. GPS Explorer, a software package developed for iTRAQ, assumes normality in the peptide ratio for a protein once an outlier filter is applied (18). The iTRAQ package ProQuant assumes that peptide ratio data for a protein follow a log-normal distribution (19). Averaging can be via mean (20), weighted average (21, 22), or weighted correlation (23). Some of these methods try to take into account the varying precision of the peptide measurements. There are many different ideas of how to process peptide data, but as yet no systematic study has been completed to guide analysis and ensure the methods being utilized are appropriate.The quality of a quantitation method can be considered in terms of precision, which refers to how well repeated measurements agree with each other, and accuracy, which refers to how much they on average deviate from the true value. Both of these types of variability are inherent to the measurement process. Precision is affected by random errors, non-reproducible and unpredictable fluctuations around the true value. (In)accuracy, by contrast, is caused by systematic biases that go consistently in the same direction. In iTRAQ, systematic biases can arise because of inconsistencies in iTRAQ labeling efficiency and protein digestion (22). Typically, ratiometric normalization has been used to address this tag bias where all peptide ratios are multiplied by a global normalization factor determined to center the ratio distribution on 1 (19, 22). Even after such normalization, concerns have been raised that iTRAQ has imperfect accuracy with ratios shrunken toward 1, and this underestimation has been reported across multiple MS platforms (23–27). It has been suggested that this underestimation arises from co-eluting peptides with similar m/z values, which are co-selected during ion selection and co-fragmented during CID (23, 27). As the majority of these will be at a 1:1 ratio across the reporter ion tags (as required for normalization in iTRAQ experiments), they will contribute a background value equally to each of the iTRAQ reporter ion signals and diminish the computed ratios.With regard to random errors, iTRAQ data are seen to exhibit heterogeneity of variance; that is the variance of the signal depends on its mean. In particular, the coefficient of variation (CV) is higher in data from low intensity peaks than in data from high intensity peaks (16, 22, 23). This has also been observed in other MS-based quantitation techniques when quantifying from the MS signal (28–30). Different approaches have been proposed to model the variance heterogeneity. Pavelka et al. (31) used a power law global error model in conjunction with quantitation data derived from spectral counts. Other authors have proposed that the higher CV at low signal arises from the majority of MS instrumentation measuring ion counts as whole numbers (32). Anderle et al. (28) described a two-component error model in which Poisson statistics of ion counts measured as whole numbers dominate at the low intensity end of the dynamic range and multiplicative effects dominate at the high intensity end and demonstrated its fit to label-free LC-MS quantitation data. Previously, in the 1990s, Rocke and Lorenzato (29) proposed a two-component additive-multiplicative error model in an environmental toxin monitoring study utilizing gas chromatography MS.How can the variance heterogeneity be addressed in the data analysis? Some of the current approaches include outlier removal (18, 25), weighted means (21, 22), inclusion filters (16, 22), logarithmic transformation (19), and weighted correlation analysis (23). Outlier removal methods, for example using Dixon''s test, assume a normal distribution for which there is little empirical basis. The inclusion filter method, where low intensity data are excluded, reduces the protein coverage considerably if the heterogeneity is to be significantly reduced. The weighted mean method results in higher intensity readings contributing more to the weighted mean than readings from low intensity readings. Filtering, outlier removal, and weighted methods are of limited use for peptides for which only a few low intensity readings were made; however, such cases typically dominate the data sets. Even with a logarithmic transformation, heterogeneity has been reported for iTRAQ data (16, 19, 22). Current methods struggle to address the issue and to maintain sensitivity.Here we investigate the data analysis issues that relate to precision and accuracy in quantitation and propose a robust methodology that is designed to make use of all data without ad hoc filtering rules. The additive-multiplicative model mentioned above motivates the so-called generalized logarithm transformation, a transformation that addresses heterogeneity of variance by approximately stabilizing the variance of the transformed signal across its whole dynamic range (33). Huber et al. (33) provided an open source software package, variance-stabilizing normalization (VSN), that determines the data-dependent transformation parameters. Here we report that the application of this transformation is beneficial for the analysis of iTRAQ data. We investigated the error structure of iTRAQ quantitation data using different peak identification and quantitation packages, LC-MS/MS data collection systems, and both the 4-plex and 8-plex iTRAQ systems. The usefulness of the VSN transformation to address heterogeneity of variance was demonstrated. Furthermore, we considered the correlations between multiple, peptide-level readings for the same protein and proposed a method to summarize them to a protein abundance estimate. We considered same-same comparisons to assess the magnitude of experimental variability and then used a set of complex biological samples whose biology has been well characterized to assess the power of the method to detect true differential abundance. We assessed the accuracy of the system with a four-protein mixture at known ratios spanning a -fold change expression range of 1–4. From this, we proposed a methodology to address the accuracy issues of iTRAQ.