Understanding uncontrolled severe allergic asthma by integration of omic and clinical data.

BACKGROUND: Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features. METHODS: Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms. RESULTS: Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy. CONCLUSIONS: UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype.

Multi-omics analysis points to altered platelet functions in severe food-associated respiratory allergy.

BACKGROUND: Prevalence and severity of allergic diseases have increased worldwide. To date, respiratory allergy phenotypes are not fully characterized and, along with inflammation progression, treatment is increasingly complex and expensive. Profilin sensitization constitutes a good model to study the progression of allergic inflammation. Our aim was to identify the underlying mechanisms and the associated biomarkers of this progression, focusing on severe phenotypes, using transcriptomics and metabolomics. METHODS: Twenty-five subjects were included in the study. Plasma samples were analyzed using gas and liquid chromatography coupled to mass spectrometry (GC-MS and LC-MS, respectively). Individuals were classified in four groups-"nonallergic," "mild," "moderate," and "severe"-based on their clinical history, their response to an oral challenge test with profilin, and after a refinement using a mathematical metabolomic model. PBMCs were used for microarray analysis. RESULTS: We found a set of transcripts and metabolites that were specific for the "severe" phenotype. By metabolomics, a decrease in carbohydrates and pyruvate and an increase in lactate were detected, suggesting aerobic glycolysis. Other metabolites were incremented in "severe" group: lysophospholipids, sphingosine-1-phosphate, sphinganine-1-phosphate, and lauric, myristic, palmitic, and oleic fatty acids. On the other hand, carnitines were decreased along severity. Significant transcripts in the "severe" group were found to be downregulated and were associated with platelet functions, protein synthesis, histone modification, and fatty acid metabolism. CONCLUSION: We have found evidence that points to the association of severe allergic inflammation with platelet functions alteration, together with reduced protein synthesis, and switch of immune cells to aerobic glycolysis.

Stereochemical and structural effects of (2R,6R)-hydroxynorketamine on the mitochondrial metabolome in PC-12 cells.

BACKGROUND: Impairment in mitochondrial biogenesis and function plays a key role in depression and anxiety, both of which being associated with changes in fatty acid and phospholipid metabolism. The antidepressant effects of (R,S)-ketamine have been linked to its conversion into (2S,6S;2R,6R)-hydroxynorketamine (HNK); however, the connection between structure and stereochemistry of ketamine and HNK in the mitochondrial homeostatic response has not yet been fully elucidated at a metabolic level. METHODS: We used a multi-platform, non-targeted metabolomics approach to study the change in mitochondrial metabolome of PC-12 cells treated with ketamine and HNK enantiomers. The identified metabolites were grouped into pathways in order to assess global responses. RESULTS: Treatment with (2R,6R)-HNK elicited the significant change in 49 metabolites and associated pathways implicated in fundamental mitochondrial functions such as TCA cycle, branched-chain amino acid biosynthetic pathway, glycoxylate metabolic pathway, and fatty acid ß-oxidation. The affected metabolites included glycerate, citrate, leucine, N,N-dimethylglycine, 3-hexenedioic acid, and carnitine and attenuated signals associated with 9 fatty acids and elaidic acid. Important metabolites involved in the purine and pyrimidine pathways were also affected by (2R-6R)-HNK. This global metabolic profile was not as strongly impacted by treatment with (2S,6S)-HNK, (R)- and (S)-ketamine and in some instances opposite effects were observed. CONCLUSIONS: The present data provide an overall view of the metabolic changes in mitochondrial function produced by (2R,6R)-HNK and related ketamine compounds and offer an insight into the source of the observed variance in antidepressant response elicited by the compounds.

In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics.

The role of non-targeted metabolomics with its discovery power is constantly growing in many different fields of science. However, its biggest advantage of uncovering the unexpected is turning into one of its biggest bottlenecks, particularly in metabolite identification. Among different methods for metabolite identification or ID confirmation, tandem MS analysis plays a very important role. However, this method is limited to only certain types of MS analysers, making for example TOF-MS inaccessible for this type of metabolite identification. To overcome this, in-source fragmentation has been used to fragment molecules and obtain product ions. Since the molecule of interest is not isolated prior to its fragmentation, the acquired spectrum contains many different signals arising from the fragmentation of all compounds present in the sample. Therefore, to assign product ions to their precursors, a novel use of correlation analysis was tested with r ≥0.9 as an assignation of a product ion belonging to the precursor. This method and chosen cut-off was tested on three different sample complexity levels: conducting the analysis on a single standard, mix of co-eluting standards and on a plasma sample. Obtained results clearly proved the effectiveness of the proposed methodology for metabolite ID confirmation. Moreover, the proposed strategy can be successfully applied for semi-quantification of co-eluting molecules with the same monoisotopic mass but that differ in fragmentation pattern. The proposed methodology can greatly improve the robustness and throughput of identification in metabolomics studies by use of TOF-MS, which is crucial to obtain meaningful and trustful results.

From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS.

Application of high-throughput technologies in metabolomics studies increases the quantity of data obtained, which in turn imposes several problems during data analysis. Correctly and clearly addressed biological question and comprehensive knowledge about data structure and properties are definitely necessary to select proper chemometric tools. However, there is a broad range of chemometric tools available for use with metabolomics data, which makes this choice challenging. Precisely performed data treatment enables valuable extraction of information and its proper interpretation. The effect of an error made at an early stage will be enhanced throughout the later stages, which in combination with other errors made at each step can accumulate and significantly affect the data interpretation. Moreover, adequate application of these tools may help not only to detect, but sometimes also to correct, biological, analytical, or methodological errors, which may affect truthfulness of obtained results. This report presents steps and tools used for LC-MS based metabolomics data extraction, reduction, and visualization. Following such steps as data reprocessing, data pretreatment, data treatment, and data revision, authors want to show how to extract valuable information and how to avoid misinterpretation of results obtained. The purpose of this work was to emphasize problematic characteristics of metabolomics data and the necessity for their attentive and precise treatment. The dataset used to illustrate metabolomics data properties and to illustrate major data treatment challenges was obtained utilizing an animal model of control and diabetic rats, both with and without rosemary treatment. Urine samples were fingerprinted employing LC-QTOF-MS.

Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility.

The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals remain uninfected despite high-risk exposures. To understand lipid and metabolite profiles related with COVID-19 susceptibility and disease progression. We collected samples from an exceptional group of unvaccinated healthcare workers heavily exposed to SARS-CoV-2 but not infected ('non-susceptible') and subjects who became infected during the follow-up ('susceptible'), including non-hospitalized and hospitalized patients with different disease severity providing samples at early disease stages. Then, we analyzed their plasma metabolomic profiles using mass spectrometry coupled with liquid and gas chromatography. We show specific lipids profiles and metabolites that could explain SARS-CoV-2 susceptibility and COVID-19 severity. More importantly, non-susceptible individuals show a unique lipidomic pattern characterized by the upregulation of most lipids, especially ceramides and sphingomyelin, which could be interpreted as markers of low susceptibility to SARS-CoV-2 infection. This study strengthens the findings of other researchers about the importance of studying lipid profiles as relevant markers of SARS-CoV-2 pathogenesis.

Metabolomic approach with LC-QTOF to study the effect of a nutraceutical treatment on urine of diabetic rats.

The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. In that sense, rosemary extracts have long been recognized as having antioxidant properties, and folic acid may be able to improve endothelial progenitor cell function. A mixture containing both has been tested as a possible nutraceutical to improve health complications in diabetes. We have developed the methodology to evaluate metabolic changes in the urine of streptozotocin-induced diabetic rats after supplementing their diet with rosemary extract obtained with supercritical fluids (SFE) containing 10% folic acid in an acute but short-term study. It has been done with a metabolomics approach using LC-QTOF as an analytical tool. About 20 endogenous metabolites have been identified by databases and MS/MS showing statistically significant changes. Among them, several amino acids and their metabolites point to changes due to the effect of the gut microbiota. In addition, the comparison between control and streptozotocin-diabetic rats has permitted the showing of some metabolic coincidences between type 1 diabetes and other (possible) autoimmune diseases such as autism and/or Crohn's disease, and the nutraceutical intervention has succeeded in inducing changes in such biomarkers.

Metabolomic approach with LC-MS reveals significant effect of pressure on diver's plasma.

Professional and recreational diving are growing activities in modern life. Diving has been associated with increased prevalence of stroke, hypertension, asthma, diabetes, or bone necrosis. We evaluated the effect of increased pressure equivalent to diving at 30 and 60 m for 30 min in two groups of divers using an untargeted approach with LC-MS fingerprinting of plasma. We found over 100 metabolites to be altered in plasma post exposure and after the corresponding decompression procedures. Among them, a group of lysophosphatidylcholines and lysophosphatidylethanolamines were increased, including lysoplasmalogen, a thrombosis promoter, together with changes in metabolic rate-associated molecules such as acylcarnitines and hemolysis-related compounds. Moreover, three metabolites that could be associated to bone degradation show different intensities between experimental groups. Ultimately, this nontargeted, short-term study opens the possibility of discovering markers of long-term effect of pressure that could be employed in routine health control of divers and could facilitate the development of safer decompression procedures.

Chemometric analysis of urine fingerprints acquired by liquid chromatography-mass spectrometry and capillary electrophoresis: application to the schistosomiasis mouse model.

Urine fingerprints from Schistosoma mansoni infected and control animals were acquired with ultra performance liquid chromatography-MS (UPLC-MS) and compared with the urine fingerprints obtained by CE by applying the same set of multivariate analysis tools. Principal component analysis of the aligned data provided a time trajectory where the infection was observed after 30 days with UPLC-MS and CE. Two main markers describing infected and control, respectively - phenyl acetyl glycine (PAG) and hippurate - were selected to illustrate the use of orthogonal partial least-square discriminant analysis in determining the discriminatory confidence. PAG was found to be significantly related to the disease (high covariance and correlation), whereas hippurate was found to be nonsignificant as an indicator. Orthogonal partial least-square discriminant analysis models were validated for sensitivity and specificity. Multivariate data analysis derived from two different detection systems showed that CE-UV and UPLC-MS found equivalent results. This work gives additional mechanistic insight into the progress of the S. mansoni infection; the biochemical role and specificity of PAG as a biomarker is yet to be determined.

Chemometric and biological validation of a capillary electrophoresis metabolomic experiment of Schistosoma mansoni infection in mice.

Metabonomic and metabolomic studies are increasingly utilized for biomarker identification in different fields, including biology of infection. The confluence of improved analytical platforms and the availability of powerful multivariate analysis software have rendered the multiparameter profiles generated by these omics platforms a user-friendly alternative to the established analysis methods where the quality and practice of a procedure is well defined. However, unlike traditional assays, validation methods for these new multivariate profiling tools have yet to be established. We propose a validation for models obtained by CE fingerprinting of urine from mice infected with the blood fluke Schistosoma mansoni. We have analysed urine samples from two sets of mice infected in an inter-laboratory experiment where different infection methods and animal husbandry procedures were employed in order to establish the core biological response to a S. mansoni infection. CE data were analysed using principal component analysis. Validation of the scores consisted of permutation scrambling (100 repetitions) and a manual validation method, using a third of the samples (not included in the model) as a test or prediction set. The validation yielded 100% specificity and 100% sensitivity, demonstrating the robustness of these models with respect to deciphering metabolic perturbations in the mouse due to a S. mansoni infection. A total of 20 metabolites across the two experiments were identified that significantly discriminated between S. mansoni-infected and noninfected control samples. Only one of these metabolites, allantoin, was identified as manifesting different behaviour in the two experiments. This study shows the reproducibility of CE-based metabolic profiling methods for disease characterization and screening and highlights the importance of much needed validation strategies in the emerging field of metabolomics.

Metabolic impact of partial hepatectomy in the non-alcoholic steatohepatitis animal model of methionine-choline deficient diet.

In the liver, obesity is often manifested by the clinical disorder of the Non-Alcoholic Fatty Liver Disease (NAFLD). A proportion of NAFLD patients develop hepatic inflammation, known as Non-Alcoholic Steatohepatitis (NASH), which can end up in cirrhosis, or Hepatocellular Carcinoma (HCC). In this scenario, partial hepatectomy (PH) is an alternative to promote liver regeneration. However, as liver regeneration is impaired in NASH patients, more knowledge about its metabolic condition is needed to improve the regenerative response of the liver in this pathological condition. Although extensively employed, the panoply of molecular alterations involved in the regenerative response of the liver after partial hepatectomy PH is far from being fully characterized. Metabolic fingerprinting (metabolomics) is a powerful tool to help in the elucidation of complex metabolic networks, by means of a blind, naïve approach to study which metabolic nodes (metabolites) show the biggest variations between conditions. The objective of the present study was to gain deeper knowledge about the metabolic processes involved in the NASH animal model, and particularly in the effect of PH by using metabolomics. For achieving such information, twelve 8-week-old male C57BL/6 J mice, fed commercial chow (control diet) or methionine and choline-Deficient diet (MCD) for three weeks were subjected to PH and sacrificed 2 weeks later. Livers were removed and submitted to metabolic profiling analysis through RP-LC/MS (qTOF), GC/MS (qTOF) and CE/MS(TOF). More than 3000 different features were detected and repeated measurements one-way ANOVA analysis was performed to unveil significant features. MCD diet induced changes (p < 0.05) in 46% of the detected features, whereas PH provoked significant changes in 85% of them. Most of the changes were detected through LC/MS and were associated to lipid metabolism. However, changes of metabolites virtually related to other metabolic routes (amino acids, carbohydrates, nucleotides) were found altered and detected by CE/MS and GC/MS. The changes associated to PH show a similar trend regardless of the diet, but in the context of the diet deficient in methionine and choline we have found results that point to a different ratio glycolysis/tricarboxylic acid cycle. Moreover, in the NASH model, the regeneration of the liver structures occurs at the expense of an increased phosphatidylethanolamines/phosphatidylcholines ratio.

The autocorrelation matrix probing biochemical relationships after metabolic fingerprinting with CE.

Fingerprinting together with statistical analysis is often employed to compare samples in metabonomic studies of a disease. Correlation algorithms can aid by extracting information based on the variation patterns of key metabolites. This information can be linked to metabolite identification or to specific up/down-regulated biochemical pathways. Matlab-based software employing the Pearson's correlation algorithm was applied to urine electropherograms from 20 mice infected with the schistosoma parasite. The fingerprints were the sum of electropherograms analysed with normal and reverse polarity, in two different modes MEKC and CZE and with two different capillaries (uncoated and polyacrylamide coated) to provide a broad picture of the samples. Hippurate, a metabolite that was depleted in the infected group and is present in both polarities, was chosen as a test variable; it correlated with itself to a p value of <0.000. Phenylacetylglycine, a metabolite shown as over expressed in the disease, was positively correlated to three metabolites in its same pathway with a correlation coefficient of 0.7 and p<0.000 to phenylalanine, 0.7 and p<0.000 to 2-hydroxyphenylacetic and 0.55 and p<0.003 to phenylacetate. The study shows that the autocorrelation matrix is able to provide extra information from data files acquired by CE analyses. It underlined an up-regulated metabolic path by association in the schistosoma infection model.

Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts.

Metabolomics, understood as the science that manages the study of compounds from the metabolism, is an essential tool for deciphering metabolic changes in disease. The experiments rely on the use of high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-ToF MS). This hyphenation has brought positive aspects such as higher sensitivity, specificity and the extension of the metabolome coverage in a single run. The analysis of a high number of samples in a single batch is currently not always feasible due to technical and practical issues (i.e., a drop of the MS signal) which result in the MS stopping during the experiment obtaining more than a single sample batch. In this situation, careful data treatment is required to enable an accurate joint analysis of multi-batch data sets. This paper summarizes the analytical strategies in large-scale metabolomic experiments; special attention has been given to QC preparation troubleshooting and data treatment. Moreover, labeled internal standards analysis and their aim in data treatment, and data normalization procedures (intra- and inter-batch) are described. These concepts are exemplified using a cohort of 165 patients from a study in asthma.

Molecular analysis of yogurt containing Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus in human intestinal microbiota.

BACKGROUND: Yogurt has traditionally been considered a probiotic-carrier food with health-promoting effects. Despite the universal assumption of this assertion, several researchers have evaluated the real capability of the yogurt bacteria Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus to survive and proliferate in the human intestine and have found contradictory results. OBJECTIVE: This double-blind crossover study assessed the qualitative and quantitative effects of fresh and heat-treated yogurt on bacterial intestinal microbiota from healthy subjects. DESIGN: The subjects were divided into experimental (n=63) and control (n=16) groups. The experimental group consumed fresh and heat-treated yogurt for 15 d according to a crossover design with a washout period of 2 wk. Three different fecal samples per individual were recovered: at baseline, after fresh yogurt intake, and after heat-treated yogurt intake. Qualitative changes in microbiota were studied by denaturing gel gradient electrophoresis (DGGE) with universal and lactic acid bacteria (LAB) 16S-rRNA primers. Quantitative changes in LAB, Clostridium coccoides, Clostridium perfringens, and Bacteroides groups were analyzed by real-time polymerase chain reaction. RESULTS: A particular DGGE stable band pattern was observed in each sample. No significant qualitative differences were detected in any fecal sample. However, a significantly higher density of LAB and C. perfringens and a significant decrease in the density of Bacteroides was observed after consumption of both types of yogurt. Microbiota density was not significantly different between the fresh and heat-treated yogurt groups, except for LAB, which was significantly greater in the fresh yogurt group. CONCLUSION: The main change in human microbiota observed after yogurt consumption was an increase in the density of LAB and C. perfringens to the detriment of Bacteroides. Bacterial changes were not different after the consumption of fresh and heat-treated yogurt.

Fingerprinting of human bile during liver transplantation by capillary electrophoresis.

Increasing rates of success in liver transplantation have increased the number of cases considered. However, liver post-transplant graft dysfunction of liver transplants (TXs) is not fully understood and by applying holistic approaches we can investigate metabolic change deriving from confounding factors such as liver fat content, ischaemia time, donor age, recipient's health, etc. Twenty-six hepatic bile samples taken from liver donors and recipients were retrieved from a total of six TXs, from these one recipient underwent post-graft dysfunction. CE was employed to fingerprint bile collected at 10 min increments in the donors and in the recipients. The electropherograms of these samples were aligned and normalised using correlation optimised warping algorithms and modelled with multivariate techniques. The resulting metabolic signatures were compared; in general donors and recipients showed distinct fingerprints and clustered separately. When a partial least square discriminant analysis (PLS-DA) model was constructed between donor and recipient's samples, a recipient of a 32 year old liver with normal steatosis, and shortest cold ischaemia time showed as the observation nearest to its donor observation, denoting minimal metabolic change. This study proposes CE fingerprinting of human bile as a promising technique to help unravel the complex metabolic pathways involved during transplantation.

Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis.

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.

GC-MS based Gestational Diabetes Mellitus longitudinal study: Identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers.

Gestational Diabetes Mellitus (GDM) causes severe short- and long-term complications for the mother, fetus and neonate, including type 2-diabetes (T2DM) later in life. In this pilot study, GC-Q/MS analysis was applied for plasma metabolomics fingerprinting of 24 healthy and 24 women with GDM at different stages of gestation (second and third trimester) and postpartum (one and three months). Multivariate (unsupervised and supervised) statistical analysis was performed to investigate variance in the data, identify outliers and for unbiased assessment of data quality. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls both in the 2nd and 3rd trimesters of gestation. However, metabolic profiles tended to be similar after delivery. Follow up of these women revealed that 4 of them developed T2DM within 2 years postpartum. Multivariate PLS-DA models limited to women with GDM showed clear separation 3 months postpartum. In the 2nd trimester of gestation there was also a clear separation between GDM women that were normoglycemic after pregnancy and those with recognized postpartum T2DM. Metabolites that had the strongest discriminative power between these groups in the 2nd trimester of gestation were 2-hydroxybutyrate, 3-hydroxybutyrate, and stearic acid. We have described, that early GDM comprises metabotypes that are associated with the risk of future complications, including postpartum T2DM. In this pilot study, we provide evidence that 2-hydroxybutyrate and 3-hydroxybutyrate may be considered as future prognostic biomarkers to predict the onset of diabetic complications in women with gestational diabetes after delivery.

Acanthamoeba spp. in Contact Lenses from Healthy Individuals from Madrid, Spain.

PURPOSE: Acanthamoeba keratitis (AK) is a painful and potentially blinding corneal infection caused by Acanthamoeba spp. In Madrid, environmental studies have demonstrated a high presence of these free-living amoebae in tap water. Since most of AK cases occur in contact lenses (CL) wearers with inadequate hygiene habits, the presence of Acanthamoeba in discarded CL has been studied and compared with other common etiological agents of keratitis, such as Pseudomonas aeruginosa and Staphylococcus aureus. METHODS: One hundred and seventy-seven healthy individuals from Madrid contributed their discarded CL and answered a questionnaire on hygiene habits. DNA was extracted from the CL solution and analyzed by real-time PCR for Acanthamoeba, Pseudomonas aeruginosa and Staphylococcus aureus. These CL and their solutions were also cultured on non-nutrient agar to isolate Acanthamoeba. RESULTS: Among the 177 samples, Acanthamoeba DNA was detected in 87 (49.2%), P. aeruginosa DNA in 14 (7.9%) and S. aureus DNA in 19 (10.7%). Cultivable amoebae, however, were observed in only one sample (0.6%). This isolate was genotyped as T4. The habits reported by this CL owner included some recognized risk factors for AK, but in this study only the practice of "not cleaning the CL case" presented some statistical significant association with Acanthamoeba DNA presence. Detection of the investigated bacterial DNA did not demonstrate statistical significant association with the studied practices, but the presence of P. aeruginosa revealed a possible inhibition of Acanthamoeba in these samples. CONCLUSIONS: The PCR results suggest a high presence of Acanthamoeba spp. in healthy CL wearers from Madrid, but we can assume that CL solutions are properly disinfecting the CL since only 1.1% of the positive PCR samples correspond to viable amoebae and, after four years, only one participant reported stronger ocular problems. Nevertheless, more studies are necessary to corroborate this hypothesis.

First Case of Legionnaire's Disease Caused by Legionella anisa in Spain and the Limitations on the Diagnosis of Legionella non-pneumophila Infections.

Legionnaires' disease is a severe form of pneumonia, with worldwide relevance, caused by Legionella spp. Approximately 90% of all cases of legionellosis are caused by Legionella pneumophila, but other species can also be responsible for this infection. These bacteria are transmitted by inhalation of aerosols or aspiration of contaminated water. In Spain, environmental studies have demonstrated the presence of Legionella non-pneumophila species in drinking water treatment plants and water distribution networks. Aware that this evidence indicates a risk factor and the lack of routine assays designed to detect simultaneously diverse Legionella species, we analyzed 210 urine samples from patients presenting clinical manifestations of pneumonia using a semi-nested PCR for partial amplification of the 16S rDNA gene of Legionella and a diagnostic method used in hospitals for Legionella antigen detection. In this study, we detected a total of 15 cases of legionellosis (7.1%) and the first case of Legionnaires' disease caused by L. anisa in Spain. While the conventional method used in hospitals could only detect four cases (1.9%) produced by L. pneumophila serogroup 1, using PCR, the following species were identified: Legionella spp. (10/15), L. pneumophila (4/15) and L. anisa (1/15). These results suggest the need to change hospital diagnostic strategies regarding the identification of Legionella species associated with this disease. Therefore, the detection of Legionella DNA by PCR in urine samples seems to be a suitable alternative method for a sensitive, accurate and rapid diagnosis of Legionella pneumonia, caused by L. pneumophila and also for L. non-pneumophila species.