Targeted Lipid Profiling Discovers Plasma Biomarkers of Acute Brain Injury

Prior efforts to identify a blood biomarker of brain injury have relied almost exclusively on proteins; however their low levels at early time points and poor correlation with injury severity have been limiting. Lipids, on the other hand, are the most abundant molecules in the brain and readily cross the blood-brain barrier. We previously showed that certain sphingolipid (SL) species are highly specific to the brain. Here we examined the feasibility of using SLs as biomarkers for acute brain injury. A rat model of traumatic brain injury (TBI) and a mouse model of stroke were used to identify candidate SL species though our mass-spectrometry based lipid profiling approach. Plasma samples collected after TBI in the rat showed large increases in many circulating SLs following injury, and larger lesions produced proportionately larger increases. Plasma samples collected 24 hours after stroke in mice similarly revealed a large increase in many SLs. We constructed an SL score (sum of the two SL species showing the largest relative increases in the mouse stroke model) and then evaluated the diagnostic value of this score on a small sample of patients (n = 14) who presented with acute stroke symptoms. Patients with true stroke had significantly higher SL scores than patients found to have non-stroke causes of their symptoms. The SL score correlated with the volume of ischemic brain tissue. These results demonstrate the feasibility of using lipid biomarkers to diagnose brain injury. Future studies will be needed to further characterize the diagnostic utility of this approach and to transition to an assay method applicable to clinical settings.     

Identification of Targeted Analyte Clusters for Studies of Schizophrenia

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analyzing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease-specific biomarkers and potential novel therapeutic strategies.Schizophrenia is a debilitating neuropsychiatric disorder that affects more than 1% of the world population and costs hundreds of billions of United States dollars in healthcare provision and lost earnings (1). The diagnosis of this disease has not changed substantially over several decades and currently relies on subjective psychopathological ratings such as the Diagnostic and Statistical Manual of Mental Disorders (DSM)1-IV. Thus, diagnosis can be complicated by the presence of overlapping symptoms frequently occurring in other psychiatric illnesses such as bipolar disorder (BD) and major depressive disorder (MDD) and by the presence of confounding factors such as drug abuse and co-morbidities. This often results in diagnosis being delayed for several months to years. A delay in establishing an accurate diagnosis can have serious deleterious implications because a late or imprecise diagnosis can contribute to unsatisfactory outcomes to currently used drug therapies and to higher rates of relapse (2). Most importantly, more than half of schizophrenia subjects develop a progressive course of the disease associated with deficit symptoms (3).In contrast, early therapeutic intervention holds promise in preventing or diminishing such effects (4–6). An empirical assay for early and accurate diagnosis of schizophrenia would deliver improved patient outcomes and reduce the costs of the disease for healthcare services and society (7–9). Such an assay could also provide a means of stratifying patients and monitoring drug responses and may also lead to the development of translational medicine tools that are critical for discovery of novel therapeutic strategies. Molecular profiling methods that afford the simultaneous measurement of multiple analytes in clinical and preclinical samples have considerable promise in this endeavor. These methods have been aimed predominantly at identifying individual molecules that show differences in expression between the disease and control conditions. However, such studies have often been fraught with small fold-changes in analyte levels, a common obstacle when investigating complex neuropsychiatric disorders (10–12). Thus, standard statistical techniques such as t tests will not be able to explore patterned behaviors involving proteins that have subtle expression changes but still contribute to the development of schizophrenia.The main objective of this study was to determine whether unique patterns of biomarkers can be identified for subjects with first onset antipsychotic-naïve schizophrenia. Analyte expression lists were generated using the Multi-Analyte Profiling (MAP®) fluorescent bead-based technology for profiling serum samples from 77 male schizophrenia patients and 66 matched male controls. For comparison with other psychiatric disorders, we also analyzed the serum samples of 13 male BD and 17 male MDD patients. In parallel, serum samples from four relevant animal models were also profiled for comparison with the human disease state. Analysis of the respective expression lists was carried out using non-linear statistical analysis, which identifies small sets of analytes called targeted analyte clusters (TACs) that have the power to discriminate the patients from normal controls. We present here the performance of these clusters for diagnosis of schizophrenia. In addition, we show how this method can also contribute to increasing our understanding of the etiology of the disorder by determining its ability to classify various preclinical models of psychiatric disorders. The biological pathways associated with these clusters are discussed with their relevance to schizophrenia.     

Plasma Protein Pentosidine and Carboxymethyllysine, Biomarkers for Age-related Macular Degeneration

Age-related macular degeneration (AMD) causes severe vision loss in the elderly; early identification of AMD risk could help slow or prevent disease progression. Toward the discovery of AMD biomarkers, we quantified plasma protein N^ε-carboxymethyllysine (CML) and pentosidine from 58 AMD and 32 control donors. CML and pentosidine are advanced glycation end products that are abundant in Bruch membrane, the extracellular matrix separating the retinal pigment epithelium from the blood-bearing choriocapillaris. We measured CML and pentosidine by LC-MS/MS and LC-fluorometry, respectively, and found higher mean levels of CML (∼54%) and pentosidine (∼64%) in AMD (p < 0.0001) relative to normal controls. Plasma protein fructosyl-lysine, a marker of early glycation, was found by amino acid analysis to be in equal amounts in control and non-diabetic AMD donors, supporting an association between AMD and increased levels of CML and pentosidine independent of other diseases like diabetes. Carboxyethylpyrrole (CEP), an oxidative modification from docosahexaenoate-containing lipids and also abundant in AMD Bruch membrane, was elevated ∼86% in the AMD cohort, but autoantibody titers to CEP, CML, and pentosidine were not significantly increased. Compellingly higher mean levels of CML and pentosidine were present in AMD plasma protein over a broad age range. Receiver operating curves indicate that CML, CEP adducts, and pentosidine alone discriminated between AMD and control subjects with 78, 79, and 88% accuracy, respectively, whereas CML in combination with pentosidine provided ∼89% accuracy, and CEP plus pentosidine provided ∼92% accuracy. Pentosidine levels appeared slightly altered in AMD patients with hypertension and cardiovascular disease, indicating further studies are warranted. Overall this study supports the potential utility of plasma protein CML and pentosidine as biomarkers for assessing AMD risk and susceptibility, particularly in combination with CEP adducts and with concurrent analyses of fructosyl-lysine to detect confounding factors.Age-related macular degeneration (AMD)¹ is a progressive, multifactorial disease and a major cause of severe vision loss in the elderly (1). Deposition of debris (drusen) in the macular region of Bruch membrane, the extracellular matrix separating the choriocapillaris from the retinal pigment epithelium (RPE), is an early, hallmark risk factor of AMD. The disease can progress to advanced dry AMD (geographic atrophy), which is characterized by regional degeneration of photoreceptor and RPE cells, or to advanced wet AMD (choroidal neovascularization (CNV)), which is characterized by abnormal blood vessels growing from the choriocapillaris through Bruch membrane beneath the retina. CNV accounts for over 80% of debilitating vision loss in AMD; however, only 10–15% of AMD cases progress to CNV.There is growing consensus that AMD is an age-related inflammatory disease involving dysregulation of the complement system; however, triggers of the inflammatory response have yet to be well defined. Oxidative stress appears to be involved as smoking significantly increases the risk of AMD (2), antioxidant vitamins can selectively slow AMD progression (3), and a host of oxidative protein and DNA modifications have been detected at elevated levels in AMD Bruch membrane, drusen, retina, RPE, and plasma (4–11). Oxidative protein modifications like carboxyethylpyrrole (CEP) and N^ε-carboxymethyllysine (CML), both elevated in AMD Bruch membrane, stimulate neovascularization in vivo (12, 13), suggesting possible roles in CNV. Other studies have shown that mice immunized with CEP protein modifications develop an AMD-like phenotype (14). Accordingly oxidative modifications may be catalysts or triggers of AMD pathology (6).AMD has long been hypothesized to be a systemic disease (15) based in part on the presence of retinal drusen in patients with membranoproliferative glomerulonephritis type II (16) and systemic complement activation in AMD (17). Support for this hypothesis also comes from mounting evidence that advanced glycation end products (AGEs) may play a role in AMD (4, 5, 7, 18, 19). AGEs are a heterogeneous group of mostly oxidative modifications resulting from the Maillard nonenzymatic glycation reaction that have been associated with age-related diseases and diabetic complications (20, 21). In 1998, CML was the first AGE to be found in AMD Bruch membrane and drusen (4). Other AGEs have since been detected in AMD ocular tissues (5, 7, 18) and in Bruch membrane, drusen, RPE, and choroidal extracellular matrix from healthy eyes (6, 22). CML, a nonfluorescent AGE, and pentosidine, a fluorescent cross-linking AGE, increase with age in Bruch membrane (18, 23). Receptors for AGEs (RAGE and AGE-R1) appear elevated on RPE and photoreceptor cells in early and advanced dry AMD (7) especially in RPE overlying drusen-like deposits on Bruch membrane (19). AGE-R3, also known as galectin-3, is elevated in AMD Bruch membrane (24).Although AMD susceptibility genes now account for over 50% of AMD cases (25), many individuals with AMD risk genotypes may never develop advanced disease with severe vision loss. Nevertheless the prevalence of advanced AMD is increasing (26). Toward the discovery of better methods to detect those at risk for advanced AMD, we quantified CML and pentosidine in plasma proteins from AMD and control patients and compared their discriminatory accuracy with plasma CEP biomarkers. CEP biomarkers have been shown to enhance the AMD predictive accuracy of genomic AMD biomarkers (11). This report shows CML and pentosidine to be elevated in AMD plasma proteins and demonstrates their potential biomarker utility in assessing AMD risk and susceptibility especially in combination with CEP biomarkers.     

Plasma Protein Profiling Reveal Osteoprotegerin as a Marker of Prognostic Impact for Colorectal Cancer

BACKGROUND: Due to difficulties in predicting recurrences in colorectal cancer stages II and III, reliable prognostic biomarkers could be a breakthrough for individualized treatment and follow-up. OBJECTIVE: To find potential prognostic protein biomarkers in colorectal cancer, using the proximity extension assays. METHODS: A panel of 92 oncology-related proteins was analyzed with proximity extension assays, in plasma from a cohort of 261 colorectal cancer patients with stage II-IV. The survival analyses were corrected for disease stage and age, and the recurrence analyses were corrected for disease stage. The significance threshold was adjusted for multiple comparisons. RESULTS: The plasma proteins expression levels had a greater prognostic relevance in disease stage III colorectal cancer than in disease stage II, and for overall survival than for time to recurrence. Osteoprotegerin was the only biomarker candidate in the protein panel that had a statistical significant association with overall survival (P = .00029). None of the proteins were statistically significantly associated with time to recurrence. CONCLUSIONS: Of the 92 analyzed plasma proteins, osteoprotegerin showed the strongest prognostic impact in patients with colorectal cancer, and therefore osteoprotegerin is a potential predictive marker, and it also could be a target for treatments.     

Serum Biomarkers of Keshan Disease Assessed Using a Protein Profiling Approach Based on ClinProt Technique

The etiology of Keshan disease (KD), an endemic myocardiopathy in regions of China, is largely unknown. To show the protein changes in serum from KD patients versus controls and idiopathic dilated cardiomyopathy (IDCM) and to search specific biological markers for differential diagnosis for KD. Serum of 65 patients with KD was compared with 29 patients with IDCM, 62 controls from KD areas and 28 controls from non-KD areas by ClinProt/MALDI-ToF technique. The genetic algorithm, quick classifier algorithm and supervised neural network algorithm methods were used to screen marker proteins and establish diagnostic model. Thirty-four differential peaks were identified in KD patients compared with the healthy controls from non-KD areas. Thirty-eight differentially peaks were identified in KD patients and controls from KD areas; and sixty-seven differentially peaks were identified in patients with KD and patients with IDCM. We believe that marker protein peaks screened in KD patients, healthy controls and IDCM patients may provide clues for the differential diagnosis and treatment of KD.     

Plasma Protein Biomarkers of Hepatocellular Carcinoma in HCV-Infected Alcoholic Patients with Cirrhosis

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers in the world, with limited options for treatment unless timely diagnosed. Chronic hepatitis C virus (HCV) infection and persistent heavy alcohol consumption are independent risk factors for HCC development, which may induce a specific protein expression pattern different from those caused separately. The aim of the study was to identify protein biomarkers for the detection of HCC in HCV-infected alcoholic patients with cirrhosis in order to improve survival. We compared protein expression profiles of plasma samples from 52 HCV-infected alcoholic patients with and without HCC, using 2-D DIGE coupled with MALDI-TOF/TOF mass spectrometry. The 2-D DIGE results were analyzed statistically using Decyder software, and verified by western-blot and ELISA. In plasma samples from HCV-infected alcoholic patients, we found significantly differential expression profiles of carboxypeptidase-N, ceruloplasmin (CP), complement component 4a (C4a), fibrinogen-alpha (FGA), immunoglobulin mu chain C region, serum albumin, and serum paraoxonase/arylesterase 1 (PON1). Deregulation of plasma/serum levels of the identified proteins was associated to HCV, ethanol consumption, and/or HCC progression. In the validation through ELISA, C4a serum concentration was increased in HCC patients (2.4±1 ng/mg vs 1.8±0.6 ng/mg; p = 0.029), being the only independent predictor of HCC in the multivariate analysis (OR = 2.15; p = 0.015), with an AUROC = 0.70. The combination of C4a, FGA, CP and PON1 improved slightly the predictive ability of C4a alone (AUROC 0.81). In conclusion, we identified proteins related to acute-phase response, oxidative stress, or immune response, whose differential expression in plasma may be attributed to the presence of HCC. Among them, C4a, and its combination with CP, FGA and PON1, could be considered as potentially reliable biomarkers for the detection of HCC in HCV-infected alcoholic patients.     

Identification of Candidate Plasma Protein Biomarkers for Cervical Cancer Using the Multiplex Proximity Extension Assay

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Highlights

• •HPV is being introduced as the primary test in cervical cancer screening programs.
• •New biomarkers are needed for co-testing of women HPV positive in screening.
• •Analysis of plasma from women with invasive cervical cancer identified a 11-marker panel.
• •This signature shows high sensitivity and specificity to identify women with cancer.

     

Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics

Background

Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer.

Methodology/Principal Findings

Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient''s age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer.

Conclusions/Significance

Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.     

Plasma Protein Biomarkers Correlated with the Development of Diet-Induced Type 2 Diabetes in Mice

Introduction

Early detection, assessment of disease progression, and application of an appropriate therapeutic intervention are all important for the care of patients with type 2 diabetes. Currently, however, there is no simple test for early detection of type 2 diabetes. Established diagnostic tests for the disease including oral glucose tolerance, fasting blood glucose, and hemoglobin A1c are relatively late markers where the disease has already progressed. Since blood is in direct contact with many tissues, we hypothesized that pathological tissue changes are likely to be reflected in proteomic profiles of plasma.

Methods

Mice were reared either on regular chow or a high-fat diet at weaning and several physiological responses (i.e., weight, fasting plasma glucose and insulin, and glucose tolerance) were monitored at regular time intervals. Plasma was collected at regular intervals for proteomic analysis by two-dimensional gel electrophoresis and subsequent mass spectrometry.

Results

Onset of hyperinsulinemia with corresponding glucose intolerance was observed in 2 weeks and fasting blood glucose levels rose significantly after 4 weeks on the high-fat diet. Many proteins were found to exist in multiple forms (isoforms). Levels of some isoforms including plasma retinol binding protein, transthyretin, Apolipoprotein A1, and kininogen showed significant changes as early as 4 weeks which coincided with the very early development of glucose intolerance.

Conclusions

These results show that a proteomic approach to study the development of type 2 diabetes may uncover unknown early post-translationally modified diagnostic and/or therapeutic protein targets.     

Neurobiological Signatures of Alcohol Dependence Revealed by Protein Profiling

Alcohol abuse causes dramatic neuroadaptations in the brain, which contribute to tolerance, dependence, and behavioral modifications. Previous proteomic studies in human alcoholics and animal models have identified candidate alcoholism-related proteins. However, recent evidences suggest that alcohol dependence is caused by changes in co-regulation that are invisible to single protein-based analysis. Here, we analyze global proteomics data to integrate differential expression, co-expression networks, and gene annotations to unveil key neurobiological rearrangements associated with the transition to alcohol dependence modeled by a Chronic Intermittent Ethanol (CIE), two-bottle choice (2BC) paradigm. We analyzed cerebral cortices (CTX) and midbrains (MB) from male C57BL/6J mice subjected to a CIE, 2BC paradigm, which induces heavy drinking and represents one of the best available animal models for alcohol dependence and relapse drinking. CIE induced significant changes in protein levels in dependent mice compared with their non-dependent controls. Multiple protein isoforms showed region-specific differential regulation as a result of post-translational modifications. Our integrative analysis identified modules of co-expressed proteins that were highly correlated with CIE treatment. We found that modules most related to the effects of CIE treatment coordinate molecular imbalances in endocytic- and energy-related pathways, with specific proteins involved, such as dynamin-1. The qRT-PCR experiments validated both differential and co-expression analyses, and the correspondence among our data and previous genomic and proteomic studies in humans and rodents substantiates our findings. The changes identified above may play a key role in the escalation of ethanol consumption associated with dependence. Our approach to alcohol addiction will advance knowledge of brain remodeling mechanisms and adaptive changes in response to drug abuse, contribute to understanding of organizational principles of CTX and MB proteomes, and define potential new molecular targets for treating alcohol addiction. The integrative analysis employed here highlight the advantages of systems approaches in studying the neurobiology of alcohol addiction.     

Biomarkers of Inflammation,Immunosuppression and Stress Are Revealed by Metabolomic Profiling of Tuberculosis Patients

Although tuberculosis (TB) causes more deaths than any other pathogen, most infected individuals harbor the pathogen without signs of disease. We explored the metabolome of >400 small molecules in serum of uninfected individuals, latently infected healthy individuals and patients with active TB. We identified changes in amino acid, lipid and nucleotide metabolism pathways, providing evidence for anti-inflammatory metabolomic changes in TB. Metabolic profiles indicate increased activity of indoleamine 2,3 dioxygenase 1 (IDO1), decreased phospholipase activity, increased abundance of adenosine metabolism products, as well as indicators of fibrotic lesions in active disease as compared to latent infection. Consistent with our predictions, we experimentally demonstrate TB-induced IDO1 activity. Furthermore, we demonstrate a link between metabolic profiles and cytokine signaling. Finally, we show that 20 metabolites are sufficient for robust discrimination of TB patients from healthy individuals. Our results provide specific insights into the biology of TB and pave the way for the rational development of metabolic biomarkers for TB.     

Serum Circulating microRNA Profiling for Identification of Potential Type 2 Diabetes and Obesity Biomarkers

Background and Aim

MicroRNAs are small non-coding RNAs that play important regulatory roles in a variety of biological processes, including complex metabolic processes, such as energy and lipid metabolism, which have been studied in the context of diabetes and obesity. Some particular microRNAs have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. The aim of this profiling study was to characterize the expression of miRNA in serum samples of obese, nonobese diabetic and obese diabetic individuals to determine whether miRNA expression was deregulated in these serum samples and to identify whether any observed deregulation was specific to either obesity or diabetes or obesity with diabetes.

Patients and Methods

Thirteen patients with type 2 diabetes, 20 obese patients, 16 obese patients with type 2 diabetes and 20 healthy controls were selected for this study. MiRNA PCR panels were employed to screen serum levels of 739 miRNAs in pooled samples from these four groups. We compared the levels of circulating miRNAs between serum pools of each group. Individual validation of the twelve microRNAs selected as promising biomarkers was carried out using RT-qPCR.

Results

Three serum microRNAs, miR-138, miR-15b and miR-376a, were found to have potential as predictive biomarkers in obesity. Use of miR-138 or miR-376a provides a powerful predictive tool for distinguishing obese patients from normal healthy controls, diabetic patients, and obese diabetic patients. In addition, the combination of miR-503 and miR-138 can distinguish diabetic from obese diabetic patients.

Conclusion

This study is the first to show a panel of serum miRNAs for obesity, and compare them with miRNAs identified in serum for diabetes and obesity with diabetes. Our results support the use of some miRNAs extracted from serum samples as potential predictive tools for obesity and type 2 diabetes.     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

Plasma miRNAs as Diagnostic and Prognostic Biomarkers for Ovarian Cancer

Background

Most (70%) epithelial ovarian cancers (EOCs) are diagnosed late. Non-invasive biomarkers that facilitate disease detection and predict outcome are needed. The microRNAs (miRNAs) represent a new class of biomarkers. This study was to identify and validate plasma miRNAs as biomarkers in EOC.

Methodology/Principal Findings

We evaluated plasma samples of 360 EOC patients and 200 healthy controls from two institutions. All samples were grouped into screening, training and validation sets. We scanned the circulating plasma miRNAs by TaqMan low-density array in the screening set and identified/validated miRNA markers by real-time polymerase chain reaction assay in the training set. Receiver operating characteristic and logistic regression analyses established the diagnostic miRNA panel, which were confirmed in the validation sets. We found higher plasma miR-205 and lower let-7f expression in cases than in controls. MiR-205 and let-7f together provided high diagnostic accuracy for EOC, especially in patients with stage I disease. The combination of these two miRNAs and carbohydrate antigen-125 (CA-125) further improved the accuracy of detection. MiR-483-5p expression was elevated in stages III and IV compared with in stages I and II, which was consistent with its expression pattern in tumor tissues. Furthermore, lower levels of let-7f were predictive of poor prognosis in EOC patients.

Conclusions/Significance

Our findings indicate that plasma miR-205 and let-7f are biomarkers for ovarian cancer detection that complement CA-125; let-7f may be predictive of ovarian cancer prognosis.     

Let-7, Mir-98 and Mir-181 as Biomarkers for Cancer and Schizophrenia

Recent evidence supports a role of microRNAs in cancer and psychiatric disorders such as schizophrenia and bipolar disorder, through their regulatory role on the expression of multiple genes. The rather rare co-morbidity of cancer and schizophrenia is an old hypothesis which needs further research on microRNAs as molecules that might exert their oncosuppressive or oncogenic activity in the context of their role in psychiatric disorders. The expression pattern of a variety of different microRNAs was investigated in patients (N = 6) suffering from schizophrenia termed control, patients with a solid tumor (N = 10) and patients with both schizophrenia and tumor (N = 8). miRNA profiling was performed on whole blood samples using the miRCURY LNA microRNA Array technology (6th & 7th generation). A subset of 3 microRNAs showed a statistically significant differential expression between the control and the study groups. Specifically, significant down-regulation of the let-7p-5p, miR-98-5p and of miR-183-5p in the study groups (tumor alone and tumorand schizophrenia) was observed (p<0.05). The results of the present study showed that let-7, miR-98 and miR-183 may play an important oncosuppressive role through their regulatory impact in gene expression irrespective of the presence of schizophrenia, although a larger sample size is required to validate these results. Nevertheless, further studies are warranted in order to highlight a possible role of these and other micro-RNAs in the molecular pathways of schizophrenia.     

Identification of Cryptic Anopheles Mosquito Species by Molecular Protein Profiling

Vector control is the mainstay of malaria control programmes. Successful vector control profoundly relies on accurate information on the target mosquito populations in order to choose the most appropriate intervention for a given mosquito species and to monitor its impact. An impediment to identify mosquito species is the existence of morphologically identical sibling species that play different roles in the transmission of pathogens and parasites. Currently PCR diagnostics are used to distinguish between sibling species. PCR based methods are, however, expensive, time-consuming and their development requires a priori DNA sequence information. Here, we evaluated an inexpensive molecular proteomics approach for Anopheles species: matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). MALDI-TOF MS is a well developed protein profiling tool for the identification of microorganisms but so far has received little attention as a diagnostic tool in entomology. We measured MS spectra from specimens of 32 laboratory colonies and 2 field populations representing 12 Anopheles species including the A. gambiae species complex. An important step in the study was the advancement and implementation of a bioinformatics approach improving the resolution over previously applied cluster analysis. Borrowing tools for linear discriminant analysis from genomics, MALDI-TOF MS accurately identified taxonomically closely related mosquito species, including the separation between the M and S molecular forms of A. gambiae sensu stricto. The approach also classifies specimens from different laboratory colonies; hence proving also very promising for its use in colony authentication as part of quality assurance in laboratory studies. While being exceptionally accurate and robust, MALDI-TOF MS has several advantages over other typing methods, including simple sample preparation and short processing time. As the method does not require DNA sequence information, data can also be reviewed at any later stage for diagnostic or functional patterns without the need for re-designing and re-processing biological material.     

Potential Biomarkers of Fatigue Identified by Plasma Metabolome Analysis in Rats

In the present study, prior to the establishment of a method for the clinical diagnosis of chronic fatigue in humans, we validated the utility of plasma metabolomic analysis in a rat model of fatigue using capillary electrophoresis-mass spectrometry (CE-MS). In order to obtain a fatigued animal group, rats were placed in a cage filled with water to a height of 2.2 cm for 5 days. A food-restricted group, in which rats were limited to 10 g/d of food (around 50% of the control group), was also assessed. The food-restricted group exhibited weight reduction similar to that of the fatigued group. CE-MS measurements were performed to evaluate the profile of food intake-dependent metabolic changes, as well as the profile in fatigue loading, resulting in the identification of 48 metabolites in plasma. Multivariate analyses using hierarchical clustering and principal component analysis revealed that the plasma metabolome in the fatigued group showed clear differences from those in the control and food-restricted groups. In the fatigued group, we found distinctive changes in metabolites related to branched-chain amino acid metabolism, urea cycle, and proline metabolism. Specifically, the fatigued group exhibited significant increases in valine, leucine, isoleucine, and 2-oxoisopentanoate, and significant decreases in citrulline and hydroxyproline compared with the control and food-restricted groups. Plasma levels of total nitric oxide were increased in the fatigued group, indicating systemic oxidative stress. Further, plasma metabolites involved in the citrate cycle, such as cis-aconitate and isocitrate, were reduced in the fatigued group. The levels of ATP were significantly decreased in the liver and skeletal muscle, indicative of a deterioration in energy metabolism in these organs. Thus, this comprehensive metabolic analysis furthered our understanding of the pathophysiology of fatigue, and identified potential diagnostic biomarkers based on fatigue pathophysiology.     

Depression of Neuronal Protein Synthesis Initiation by Protein Tyrosine Kinase Inhibitors

Abstract— Growth factors stimulate cellular protein synthesis, but the intracellular signaling mechanisms that regulate initiation of mRNA translation in neurons have not been clarified. A rate-limiting step in the initiation of protein synthesis is the formation of the ternary complex among GTP, eukaryotic initiation factor 2 (elF-2), and the initiator tRNA. Here we report that genistein, a specific tyrosine kinase inhibitor, decreases tyrosine kinase activity and the content of phosphotyrosine proteins in cultured primary cortical neurons. Genistein inhibits protein synthesis by >80% in a dose-dependent manner (10–80 μg/ml) and concurrently decreases ternary complex formation by 60%. At the doses investigated, genistein depresses tyrosine kinase activity and concomitantly stimulates PKC activity. We propose that a protein tyrosine kinase participates in the initiation of protein synthesis in neurons, by affecting the activity of elF-2 directly or through a protein kinase cascade.