A Multi-Omic Systems-Based Approach Reveals Metabolic Markers of Bacterial Vaginosis and Insight into the Disease

Background

Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms.

Principal Findings

16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to Dialister spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and Mobiluncus spp., and with pain, diethylene glycol and Gardnerella spp.

Conclusions

The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV.     

Metabolomic profiling in follicular fluid of patients with infertility-related deep endometriosis

Introduction

Endometriosis is an estrogen-dependent gynecological disease that causes infertility, and potential metabolomic biomarkers related to ovarian endometriosis and poor outcomes after assisted reproductive treatments are still lacking.

Objectives

The present study analyzed the metabolomic profiling of follicular fluid samples from 40 patients undergoing in vitro fertilization.

Methods

The follicular fluid samples were classified as controls (n = 22) and endometriosis patients (n = 18). The samples were submitted to Bligh and Dyer protocol followed by metabolomics analysis by ultra-performance liquid chromatography mass spectrometry. Clinical data was assessed by Students’ T-test and metabolomics data was analyzed by multivariate statistics by MetaboAnalyst 3.0 to obtain intrinsic characteristics that allowed for groups discrimination. The Receiver Operating Characteristic curve was carried out for the proposed biomarkers, aiming to determine their specificity and sensitivity, as a set and individually.

Results

From the metabolomic analysis, 20 ion masses were selected as potential biomarkers from principal component analysis, which showed that all biomarkers were more abundant in the endometriosis group when compared to controls. Tentative attribution was performed by lipid maps database, demonstrating that these potential biomarkers correspond to fatty acids, carnitines, monoacylglycerols, lysophosphatidic acids, lysophosphatidylglycerols, diacylglycerols, lysophosphatidylcholines, phosphatidylserine, lysophosphatidylinositols and Phosphatidic Acid.

Conclusion

The use of mass spectrometry-based metabolomics allowed for the identification of effective biomarkers for ovarian endometriosis, which may contribute for a better comprehension of the disease and how it affects the ovary, as well as assisting in the development of accessory tools for endometriosis diagnosis and infertility management.

     

Sources of Variability in Metabolite Measurements from Urinary Samples

Background

The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies.

Methods

We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2–3 samples per person from 17 male subjects (age 38–70) over 2–10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies.

Results

Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of “usual” levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations.

Conclusions

The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.     

Development of a Multi-Biomarker Disease Activity Test for Rheumatoid Arthritis

Background

Disease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment.

Objectives

To develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis.

Methods

Candidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.

Results

130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities.

Conclusion

We followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels.     

Identification of proteins found to be significantly altered when comparing the serum proteome from Multiple Myeloma patients with varying degrees of bone disease

Background

Bone destruction is a feature of multiple myeloma, characterised by osteolytic bone destruction due to increased osteoclast activity and suppressed or absent osteoblast activity. Almost all multiple myeloma patients develop osteolytic bone lesions associated with severe and debilitating bone pain, pathologic fractures, hypercalcemia, and spinal cord compression, as well as increased mortality. Biomarkers of bone remodelling are used to identify disease characteristics that can help select the optimal management of patients. However, more accurate biomarkers are needed to effectively mirror the dynamics of bone disease activity.

Results

A label-free mass spectrometry-based strategy was employed for discovery phase analysis of fractionated patient serum samples associated with no or high bone disease. A number of proteins were identified which were statistically significantly correlated with bone disease, including enzymes, extracellular matrix glycoproteins, and components of the complement system.

Conclusions

Enzyme-linked immunosorbent assay of complement C4 and serum paraoxonase/arylesterase 1 indicated that these proteins were associated with high bone disease in a larger independent cohort of patient samples. These biomolecules may therefore be clinically useful in assessing the extent of bone disease.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-904) contains supplementary material, which is available to authorized users.     

Non-Invasive Imaging Provides Spatiotemporal Information on Disease Progression and Response to Therapy in a Murine Model of Multiple Myeloma

Background

Multiple myeloma (MM) is a B-cell malignancy, where malignant plasma cells clonally expand in the bone marrow of older people, causing significant morbidity and mortality. Typical clinical symptoms include increased serum calcium levels, renal insufficiency, anemia, and bone lesions. With standard therapies, MM remains incurable; therefore, the development of new drugs or immune cell-based therapies is desirable. To advance the goal of finding a more effective treatment for MM, we aimed to develop a reliable preclinical MM mouse model applying sensitive and reproducible methods for monitoring of tumor growth and metastasis in response to therapy.

Material and Methods

A mouse model was created by intravenously injecting bone marrow-homing mouse myeloma cells (MOPC-315.BM) that expressed luciferase into BALB/c wild type mice. The luciferase in the myeloma cells allowed in vivo tracking before and after melphalan treatment with bioluminescence imaging (BLI). Homing of MOPC-315.BM luciferase+ myeloma cells to specific tissues was examined by flow cytometry. Idiotype-specific myeloma protein serum levels were measured by ELISA. In vivo measurements were validated with histopathology.

Results

Strong bone marrow tropism and subsequent dissemination of MOPC-315.BM luciferase⁺ cells in vivo closely mimicked the human disease. In vivo BLI and later histopathological analysis revealed that 12 days of melphalan treatment slowed tumor progression and reduced MM dissemination compared to untreated controls. MOPC-315.BM luciferase⁺ cells expressed CXCR4 and high levels of CD44 and α4β1 in vitro which could explain the strong bone marrow tropism. The results showed that MOPC-315.BM cells dynamically regulated homing receptor expression and depended on interactions with surrounding cells.

Conclusions

This study described a novel MM mouse model that facilitated convenient, reliable, and sensitive tracking of myeloma cells with whole body BLI in living animals. This model is highly suitable for monitoring the effects of different treatment regimens.     

SMA-MAP: A Plasma Protein Panel for Spinal Muscular Atrophy

Objectives

Spinal Muscular Atrophy (SMA) presents challenges in (i) monitoring disease activity and predicting progression, (ii) designing trials that allow rapid assessment of candidate therapies, and (iii) understanding molecular causes and consequences of the disease. Validated biomarkers of SMA motor and non-motor function would offer utility in addressing these challenges. Our objectives were (i) to discover additional markers from the Biomarkers for SMA (BforSMA) study using an immunoassay platform, and (ii) to validate the putative biomarkers in an independent cohort of SMA patients collected from a multi-site natural history study (NHS).

Methods

BforSMA study plasma samples (N = 129) were analyzed by immunoassay to identify new analytes correlating to SMA motor function. These immunoassays included the strongest candidate biomarkers identified previously by chromatography. We selected 35 biomarkers to validate in an independent cohort SMA type 1, 2, and 3 samples (N = 158) from an SMA NHS. The putative biomarkers were tested for association to multiple motor scales and to pulmonary function, neurophysiology, strength, and quality of life measures. We implemented a Tobit model to predict SMA motor function scores.

Results

12 of the 35 putative SMA biomarkers were significantly associated (p<0.05) with motor function, with a 13^th analyte being nearly significant. Several other analytes associated with non-motor SMA outcome measures. From these 35 biomarkers, 27 analytes were selected for inclusion in a commercial panel (SMA-MAP) for association with motor and other functional measures.

Conclusions

Discovery and validation using independent cohorts yielded a set of SMA biomarkers significantly associated with motor function and other measures of SMA disease activity. A commercial SMA-MAP biomarker panel was generated for further testing in other SMA collections and interventional trials. Future work includes evaluating the panel in other neuromuscular diseases, for pharmacodynamic responsiveness to experimental SMA therapies, and for predicting functional changes over time in SMA patients.     

Metabolomics-Based Discovery of Small Molecule Biomarkers in Serum Associated with Dengue Virus Infections and Disease Outcomes

Background

Epidemic dengue fever (DF) and dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) are overwhelming public health capacity for diagnosis and clinical care of dengue patients throughout the tropical and subtropical world. The ability to predict severe dengue disease outcomes (DHF/DSS) using acute phase clinical specimens would be of enormous value to physicians and health care workers for appropriate triaging of patients for clinical management. Advances in the field of metabolomics and analytic software provide new opportunities to identify host small molecule biomarkers (SMBs) in acute phase clinical specimens that differentiate dengue disease outcomes.

Methodology/Principal Findings

Exploratory metabolomic studies were conducted to characterize the serum metabolome of patients who experienced different dengue disease outcomes. Serum samples from dengue patients from Nicaragua and Mexico were retrospectively obtained, and hydrophilic interaction liquid chromatography (HILIC)-mass spectrometry (MS) identified small molecule metabolites that were associated with and statistically differentiated DHF/DSS, DF, and non-dengue (ND) diagnosis groups. In the Nicaraguan samples, 191 metabolites differentiated DF from ND outcomes and 83 differentiated DHF/DSS and DF outcomes. In the Mexican samples, 306 metabolites differentiated DF from ND and 37 differentiated DHF/DSS and DF outcomes. The structural identities of 13 metabolites were confirmed using tandem mass spectrometry (MS/MS). Metabolomic analysis of serum samples from patients diagnosed as DF who progressed to DHF/DSS identified 65 metabolites that predicted dengue disease outcomes. Differential perturbation of the serum metabolome was demonstrated following infection with different DENV serotypes and following primary and secondary DENV infections.

Conclusions/Significance

These results provide proof-of-concept that a metabolomics approach can be used to identify metabolites or SMBs in serum specimens that are associated with distinct DENV infections and disease outcomes. The differentiating metabolites also provide insights into metabolic pathways and pathogenic and immunologic mechanisms associated with dengue disease severity.     

Serum Metabolomic Profiling in Acute Alcoholic Hepatitis Identifies Multiple Dysregulated Pathways

Background and Objectives

While animal studies have implicated derangements of global energy homeostasis in the pathogenesis of acute alcoholic hepatitis (AAH), the relevance of these findings to the development of human AAH remains unclear. Using global, unbiased serum metabolomics analysis, we sought to characterize alterations in metabolic pathways associated with severe AAH and identify potential biomarkers for disease prognosis.

Methods

This prospective, case-control study design included 25 patients with severe AAH and 25 ambulatory patients with alcoholic cirrhosis. Serum samples were collected within 24 hours of the index clinical encounter. Global, unbiased metabolomics profiling was performed. Patients were followed for 180 days after enrollment to determine survival.

Results

Levels of 234 biochemicals were altered in subjects with severe AAH. Random-forest analysis, principal component analysis, and integrated hierarchical clustering methods demonstrated that metabolomics profiles separated the two cohorts with 100% accuracy. Severe AAH was associated with enhanced triglyceride lipolysis, impaired mitochondrial fatty acid beta oxidation, and upregulated omega oxidation. Low levels of multiple lysolipids and related metabolites suggested decreased plasma membrane remodeling in severe AAH. While most measured bile acids were increased in severe AAH, low deoxycholate and glycodeoxycholate levels indicated intestinal dysbiosis. Several changes in substrate utilization for energy homeostasis were identified in severe AAH, including increased glucose consumption by the pentose phosphate pathway, altered tricarboxylic acid (TCA) cycle activity, and enhanced peptide catabolism. Finally, altered levels of small molecules related to glutathione metabolism and antioxidant vitamin depletion were observed in patients with severe AAH. Univariable logistic regression revealed 15 metabolites associated with 180-day survival in severe AAH.

Conclusion

Severe AAH is characterized by a distinct metabolic phenotype spanning multiple pathways. Metabolomics profiling revealed a panel of biomarkers for disease prognosis, and future studies are planned to validate these findings in larger cohorts of patients with severe AAH.     

Metabolomic Profiling in LRRK2-Related Parkinson's Disease

Background

Mutations in LRRK2 gene represent the most common known genetic cause of Parkinson''s disease (PD).

Methodology/Principal Findings

We used metabolomic profiling to identify biomarkers that are associated with idiopathic and LRRK2 PD. We compared plasma metabolomic profiles of patients with PD due to the G2019S LRRK2 mutation, to asymptomatic family members of these patients either with or without G2019S LRRK2 mutations, and to patients with idiopathic PD, as well as non-related control subjects. We found that metabolomic profiles of both idiopathic PD and LRRK2 PD subjects were clearly separated from controls. LRRK2 PD patients had metabolomic profiles distinguishable from those with idiopathic PD, and the profiles could predict whether the PD was secondary to LRRK2 mutations or idiopathic. Metabolomic profiles of LRRK2 PD patients were well separated from their family members, but there was a slight overlap between family members with and without LRRK2 mutations. Both LRRK2 and idiopathic PD patients showed significantly reduced uric acid levels. We also found a significant decrease in levels of hypoxanthine and in the ratios of major metabolites of the purine pathway in plasma of PD patients.

Conclusions/Significance

These findings show that LRRK2 patients with the G2019S mutation have unique metabolomic profiles that distinguish them from patients with idiopathic PD. Furthermore, asymptomatic LRRK2 carriers can be separated from gene negative family members, which raises the possibility that metabolomic profiles could be useful in predicting which LRRK2 carriers will eventually develop PD. The results also suggest that there are aberrations in the purine pathway in PD which may occur upstream from uric acid.     

Italian cohort of patients affected by inflammatory bowel disease is characterised by variation in glycerophospholipid,free fatty acids and amino acid levels

Background

Inflammatory bowel disease is a group of pathologies characterised by chronic inflammation of the intestine and an unclear aetiology. Its main manifestations are Crohn’s disease and ulcerative colitis. Currently, biopsies are the most used diagnostic tests for these diseases and metabolomics could represent a less invasive approach to identify biomarkers of disease presence and progression.

Objectives

The lipid and the polar metabolite profile of plasma samples of patients affected by inflammatory bowel disease have been compared with healthy individuals with the aim to find their metabolomic differences. Also, a selected sub-set of samples was analysed following solid phase extraction to further characterise differences between pathological samples.

Methods

A total of 200 plasma samples were analysed using drift tube ion mobility coupled with time of flight mass spectrometry and liquid chromatography for the lipid metabolite profile analysis, while liquid chromatography coupled with triple quadrupole mass spectrometry was used for the polar metabolite profile analysis.

Results

Variations in the lipid profile between inflammatory bowel disease and healthy individuals were highlighted. Phosphatidylcholines, lyso-phosphatidylcholines and fatty acids were significantly changed among pathological samples suggesting changes in phospholipase A₂ and arachidonic acid metabolic pathways. Variations in the levels of cholesteryl esters and glycerophospholipids were also found. Furthermore, a decrease in amino acids levels suggests mucosal damage in inflammatory bowel disease.

Conclusions

Given good statistical results and predictive power of the model produced in our study, metabolomics can be considered as a valid tool to investigate inflammatory bowel disease.

     

Evidence of Uncoupling between Renal Dysfunction and Injury in Cardiorenal Syndrome: Insights from the BIONICS Study

Objective

The objective of the study was to assess urinary biomarkers of renal injury for their individual or collective ability to predict Worsening renal function (WRF) in patients with acutely decompensated heart failure (ADHF).

Methods

In a prospective, blinded international study, 87 emergency department (ED) patients with ADHF were evaluated with biomarkers of cardiac stretch (B type natriuretic peptide [BNP] and its amino terminal equivalent [NT-proBNP], ST2), biomarkers of renal function (creatinine, estimated glomerular filtration rate [eGFR]) and biomarkers of renal injury (plasma neutrophil gelatinase associated lipocalin [pNGAL], urine kidney injury molecule-1 [KIM-1], urine N-acetyl-beta-D-glucosaminidase [NAG], urine Cystatin C, urine fibrinogen). The primary endpoint was WRF.

Results

26% developed WRF; baseline characteristics of subjects who developed WRF were generally comparable to those who did not. Biomarkers of renal function and urine biomarkers of renal injury were not correlated, while urine biomarkers of renal injury correlated between each other. Biomarker concentrations were similar between patients with and without WRF except for baseline BNP. Although plasma NGAL was associated with the combined endpoint, none of the biomarker showed predictive accuracy for WRF.

Conclusions

In ED patients with ADHF, urine biomarkers of renal injury did not predict WRF. Our data suggest that a weak association exists between renal dysfunction and renal injury in this setting (Clinicaltrials.gov NCT#0150153).     

Evidences of Early Senescence in Multiple Myeloma Bone Marrow Mesenchymal Stromal Cells

Background

In multiple myeloma, bone marrow mesenchymal stromal cells support myeloma cell growth. Previous studies have suggested that direct and indirect interactions between malignant cells and bone marrow mesenchymal stromal cells result in constitutive abnormalities in the bone marrow mesenchymal stromal cells.

Design and Methods

The aims of this study were to investigate the constitutive abnormalities in myeloma bone marrow mesenchymal stromal cells and to evaluate the impact of new treatments.

Results

We demonstrated that myeloma bone marrow mesenchymal stromal cells have an increased expression of senescence-associated β-galactosidase, increased cell size, reduced proliferation capacity and characteristic expression of senescence-associated secretory profile members. We also observed a reduction in osteoblastogenic capacity and immunomodulatory activity and an increase in hematopoietic support capacity. Finally, we determined that current treatments were able to partially reduce some abnormalities in secreted factors, proliferation and osteoblastogenesis.

Conclusions

We showed that myeloma bone marrow mesenchymal stromal cells have an early senescent profile with profound alterations in their characteristics. This senescent state most likely participates in disease progression and relapse by altering the tumor microenvironment.     

Grey Matter Changes in Cognitively Impaired Parkinson's Disease Patients

Background

Cortical changes associated with cognitive decline in Parkinson''s disease (PD) are not fully explored and require investigations with established diagnostic classification criteria.

Objective

We used MRI source-based morphometry to evaluate specific differences in grey matter volume patterns across 4 groups of subjects: healthy controls (HC), PD with normal cognition (PD-NC), PD with mild cognitive impairment (MCI-PD) and PD with dementia (PDD).

Methods

We examined 151 consecutive subjects: 25 HC, 75 PD-NC, 29 MCI-PD, and 22 PDD at an Italian and Czech movement disorder centre. Operational diagnostic criteria were applied to classify MCI-PD and PDD. All structural MRI images were processed together in the Czech centre. The spatial independent component analysis was used to assess group differences of local grey matter volume.

Results

We identified two independent patterns of grey matter volume deviations: a) Reductions in the hippocampus and temporal lobes; b) Decreases in fronto-parietal regions and increases in the midbrain/cerebellum. Both patterns differentiated PDD from all other groups and correlated with visuospatial deficits and letter verbal fluency, respectively. Only the second pattern additionally differentiated PD-NC from HC.

Conclusion

Grey matter changes in PDD involve areas associated with Alzheimer-like pathology while fronto-parietal abnormalities are possibly an early marker of PD cognitive decline. These findings are consistent with a non-linear cognitive progression in PD.     

Metabolomic Derangements Are Associated with Mortality in Critically Ill Adult Patients

Objective

To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults.

Rationale

Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible.

Methods

We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study.

Results

We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations).

Conclusion

Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.     

Clinical use of biomarkers of survival in pulmonary fibrosis

Background

Biologic predictors or biomarkers of survival in pulmonary fibrosis with a worse prognosis, more specifically in idiopathic pulmonary fibrosis would help the clinician in deciding whether or not to treat since treatment carries a potential risk for adverse events. These decisions are made easier if accurate and objective measurements of the patients'' clinical status can predict the risk of progression to death.

Method

A literature review is given on different biomarkers of survival in interstitial lung disease, mainly in IPF, since this disease has the worst prognosis.

Conclusion

Serum biomarkers, and markers measured by medical imaging as HRCT, pertechnegas, DTPA en FDG-PET are not ready for clinical use to predict mortality in different forms of ILD. A baseline FVC, a change of FVC of more than 10%, and change in 6MWD are clinically helpful predictors of survival.     

Differential volatile signatures from skin, naevi and melanoma: a novel approach to detect a pathological process

Background

Early detection of melanoma is of great importance to reduce mortality. Discovering new melanoma biomarkers would improve early detection and diagnosis. Here, we present a novel approach to detect volatile compounds from skin.

Methods and Findings

We used Head Space Solid Phase Micro-Extraction (HS-SPME) and gas chromatography/mass spectrometry (GC/MS) to identify volatile signatures from melanoma, naevi and skin samples. We hypothesized that the metabolic state of tissue alters the profile of volatile compounds. Volatiles released from fresh biopsy tissue of melanoma and benign naevus were compared based on their difference in frequency distribution and their expression level. We also analyzed volatile profiles from frozen tissue, including skin and melanoma.

Conclusions

Three volatiles, 4-methyl decane, dodecane and undecane were preferentially expressed in both fresh and frozen melanoma, indicating that they are candidate biomarkers. Twelve candidate biomarkers evaluated by fuzzy logic analysis of frozen samples distinguished melanoma from skin with 89% sensitivity and 90% specificity. Our results demonstrate proof-of-principle that there is differential expression of volatiles in melanoma. Our volatile metabolomic approach will lead to a better understanding of melanoma and can enable development of new diagnostic and treatment strategies based on altered metabolism.     

Defects in mitochondrial dynamics and metabolomic signatures of evolving energetic stress in mouse models of familial Alzheimer's disease

Background

The identification of early mechanisms underlying Alzheimer''s Disease (AD) and associated biomarkers could advance development of new therapies and improve monitoring and predicting of AD progression. Mitochondrial dysfunction has been suggested to underlie AD pathophysiology, however, no comprehensive study exists that evaluates the effect of different familial AD (FAD) mutations on mitochondrial function, dynamics, and brain energetics.

Methods and Findings

We characterized early mitochondrial dysfunction and metabolomic signatures of energetic stress in three commonly used transgenic mouse models of FAD. Assessment of mitochondrial motility, distribution, dynamics, morphology, and metabolomic profiling revealed the specific effect of each FAD mutation on the development of mitochondrial stress and dysfunction. Inhibition of mitochondrial trafficking was characteristic for embryonic neurons from mice expressing mutant human presenilin 1, PS1(M146L) and the double mutation of human amyloid precursor protein APP(Tg2576) and PS1(M146L) contributing to the increased susceptibility of neurons to excitotoxic cell death. Significant changes in mitochondrial morphology were detected in APP and APP/PS1 mice. All three FAD models demonstrated a loss of the integrity of synaptic mitochondria and energy production. Metabolomic profiling revealed mutation-specific changes in the levels of metabolites reflecting altered energy metabolism and mitochondrial dysfunction in brains of FAD mice. Metabolic biomarkers adequately reflected gender differences similar to that reported for AD patients and correlated well with the biomarkers currently used for diagnosis in humans.

Conclusions

Mutation-specific alterations in mitochondrial dynamics, morphology and function in FAD mice occurred prior to the onset of memory and neurological phenotype and before the formation of amyloid deposits. Metabolomic signatures of mitochondrial stress and altered energy metabolism indicated alterations in nucleotide, Krebs cycle, energy transfer, carbohydrate, neurotransmitter, and amino acid metabolic pathways. Mitochondrial dysfunction, therefore, is an underlying event in AD progression, and FAD mouse models provide valuable tools to study early molecular mechanisms implicated in AD.     

The Association between Serum Biomarkers and Disease Outcome in Influenza A(H1N1)pdm09 Virus Infection: Results of Two International Observational Cohort Studies

Background

Prospective studies establishing the temporal relationship between the degree of inflammation and human influenza disease progression are scarce. To assess predictors of disease progression among patients with influenza A(H1N1)pdm09 infection, 25 inflammatory biomarkers measured at enrollment were analyzed in two international observational cohort studies.

Methods

Among patients with RT-PCR-confirmed influenza A(H1N1)pdm09 virus infection, odds ratios (ORs) estimated by logistic regression were used to summarize the associations of biomarkers measured at enrollment with worsened disease outcome or death after 14 days of follow-up for those seeking outpatient care (FLU 002) or after 60 days for those hospitalized with influenza complications (FLU 003). Biomarkers that were significantly associated with progression in both studies (p<0.05) or only in one (p<0.002 after Bonferroni correction) were identified.

Results

In FLU 002 28/528 (5.3%) outpatients had influenza A(H1N1)pdm09 virus infection that progressed to a study endpoint of complications, hospitalization or death, whereas in FLU 003 28/170 (16.5%) inpatients enrolled from the general ward and 21/39 (53.8%) inpatients enrolled directly from the ICU experienced disease progression. Higher levels of 12 of the 25 markers were significantly associated with subsequent disease progression. Of these, 7 markers (IL-6, CD163, IL-10, LBP, IL-2, MCP-1, and IP-10), all with ORs for the 3^rd versus 1^st tertile of 2.5 or greater, were significant (p<0.05) in both outpatients and inpatients. In contrast, five markers (sICAM-1, IL-8, TNF-α, D-dimer, and sVCAM-1), all with ORs for the 3^rd versus 1^st tertile greater than 3.2, were significantly (p≤.002) associated with disease progression among hospitalized patients only.

Conclusions

In patients presenting with varying severities of influenza A(H1N1)pdm09 virus infection, a baseline elevation in several biomarkers associated with inflammation, coagulation, or immune function strongly predicted a higher risk of disease progression. It is conceivable that interventions designed to abrogate these baseline elevations might affect disease outcome.