Serum Metabolomic Indicates Potential Biomarkers and Metabolic Pathways of Pediatric Kashin-Beck Disease

Objective To explore potential serum biomarkers of children with Kashin-Beck Disease(KBD)and the metabolic pathways to which the biomarkers belong.Methods A two-stage metabolomic study was employed.The discovery cohort included 56 patients,51 internal controls,and 50 external controls.The metabolites were determined by HPLC-(Q-TOF)-MS and confirmed by Human Metabolome Databases(HMDB)and Metlin databases.MetaboAnalyst 3.0 and the Kyoto Encyclopedia of Genes and Genomes(KEGG)database were used to analyze the metabolic pathways of the candidate metabolites.The use of HPLC-(Q-TRAP)-MS enabled quantitative detection of the target metabolites which were chosen using the discovery study and verified in another independent verification cohort of 31 patients,41 internal controls,and 50 external controls.Results Eight candidate metabolites were identified out in the discovery study,namely kynurenic acid,N-α-acetylarginine,6-hydroxymelatonin,sphinganine,ceramide,sphingosine-1 P,spermidine,and glycine.These metabolites exist in sphingolipid,glutathione,and tryptophan metabolic pathways.In the second-stage study,five candidate metabolites were validated,including kynurenic acid,N-α-acetylarginine,sphinganine,spermidine,and sphingosine-1 P.Except for spermidine,all substances exhibited low expression in the case group compared with the external control group,and the difference in levels of sphinganine,spermidine,and sphingosine-1 P was statistically significant.Conclusion The direction of change of levels of sphinganine,spermidine,and sphingosine-1 P in the two-stage study cohorts was completely consistent,and the differences were statistically significant.Therefore,these substances can be used as potential biomarkers of KBD.Furthermore,these results raise the possibility that sphingolipid metabolic pathways may be closely related to KBD.

Bioelemental patterns in the cerebrospinal fluid as potential biomarkers for neurodegenerative disorders

Neurodegenerative disorders like Parkinson＇s disease （PD） or atypi- cal Parkinsonian syndromes including the different synucleinopa- thies and tauopathies are an important burden for patients, rela- tives, care providers and incur mounting costs for the health care system in our aging society.

Egg-derived tyrosine phosphatase as a Potential Biomarker for Muscle Ageing and Degeneration in Drosophila melanogaster

Ageing is associated with declined activity of behaviors, physiology and metabolic processes （Arking, 2006）. In- vestigations in model organisms have indicated the exis- tence of ＂functional senescence＂, the progressive decline of biological functions with age and the decline in the activity may vary from tissue to tissue. Consequently, studies per- taining to the key organs/tissues whose functions deterio- rate/fail with age have led to the development of tissue specific ageing biomarkers （Grotewiel et al., 2005; Demontis et al., 2013）.

MARINOBUFAGENIN AS A POTENTIAL BIOMARKER FOR PRE-ECLAMPSIA RISK ASSESSMENT:PURIFICATION FROM BUFO MARINUS TOAD VENOM AS SUPPORT FOR A HUMAN PLASMA LC-MS/MS ASSAY

Marinobufagenin(MBG)is a bufadienolide compound belonging to the cardiac glycosides class.The bufadienolides are present in humans as well as in some plants and animals.But the major source for these compounds is located in the parotid and skin gland secretions of some toad species.MBG is acting as a human endogenous cardiac inotrope and is demonstrating

Metabonomic analysis of hepatitis B virus-induced liver failure:identification of potential diagnostic biomarkers by fuzzy support vector machine

Hepatitis B virus （HBV）-induced liver failure is an emergent liver disease leading to high mortality. The severity of liver failure may be reflected by the profile of some metabolites. This study assessed the potential of using metabolites as biomarkers for liver failure by identifying metabolites with good discriminative performance for its phenotype. The serum samples from 24 HBV-indueed liver failure patients and 23 healthy volunteers were collected and analyzed by gas chromatography-mass spectrometry （GC-MS） to generate metabolite profiles. The 24 patients were further grouped into two classes according to the severity of liver failure. Twenty-five eommensal peaks in all metabolite profiles were extracted, and the relative area values of these peaks were used as features for each sample. Three algorithms, F-test, k-nearest neighbor （KNN） and fuzzy support vector machine （FSVM） combined with exhaustive search （ES）, were employed to identify a subset of metabolites （biomarkers） that best predict liver failure. Based on the achieved experimental dataset, 93.62% predictive accuracy by 6 features was selected with FSVM-ES and three key metabolites, glyeerie acid, cis-aeonitie acid and citric acid, are identified as potential diagnostic biomarkers.

Association of insertion-deletions polymorphisms with colorectal cancer risk and clinical features

AIM To investigate the association between 16 insertiondeletions(INDEL) polymorphisms, colorectal cancer(CRC) risk and clinical features in an admixed population.METHODS O n e h u n d re d a n d fo r ty p a t i e n t s w i t h C R C a n d 140 cancer-free subjects were examined. Genomic DNA was extracted from peripheral blood samples. Polymorphisms and genomic ancestry distribution were assayed by Multiplex-PCR reaction, separated by capillary electrophoresis on the ABI 3130 Genetic Analyzer instrument and analyzed on Gene Mapper ID v3.2. Clinicopathological data were obtained by consulting the patients' clinical charts, intra-operative documentation, and pathology scoring.RESULTS Logistic regression analysis showed that polymorphism variations in IL4 gene was associated with increased CRC risk, while TYMS and UCP2 genes were associated with decreased risk. Reference to anatomical localization of tumor Del allele of NFKB1 and CASP8 were associated with more colon related incidents than rectosigmoid. In relation to the INDEL association with tumor node metastasis(TNM) stage risk, the Ins alleles of ACE, HLAG and TP53(6 bp INDEL) were associated with higher TNM stage. Furthermore, regarding INDEL association with relapse risk, the Ins alleles of ACE, HLAG, and UGT1A1 were associated with early relapse risk, as well as the Del allele of TYMS. Regarding INDEL association with death risk before 10 years, the Ins allele of SGSM3 and UGT1A1 were associated with death risk.CONCLUSION The INDEL variations in ACE, UCP2, TYMS, IL4, NFKB1, CASP8, TP53, HLAG, UGT1A1, and SGSM3 were associated with CRC risk and clinical features in an admixed population. These data suggest that this cancer panel might be useful as a complementary tool for better clinical management, and more studies need to be conducted to confirm these findings.

Label-free quantitative proteomics reveals fibrinopeptide B and heparin cofactorⅡas potential serum biomarkers in respiratory syncytial virus-infected mice treated with Qingfei oral liquid formula

Respiratory syncytial virus(RSV) is a leading cause of acute lower respiratory tract infections. Qingfei oral liquid(QFOL), a traditional Chinese medicine, is widely used in clinical treatment for RSV-induced pneumonia. The present study was designed to reveal the potential targets and mechanism of action for QFOL by exploring its influence on the host cellular network following RSV infection. We investigated the serum proteomic changes and potential biomarkers in an RSV-infected mouse pneumonia model treated with QFOL. Eighteen BALB/c mice were randomly divided into three groups: RSV pneumonia model group(M), QFOL-treated group(Q) and the control group(C). Serum proteomes were analyzed and compared using a label-free quantitative LC-MS/MS approach. A total of 172 protein groups, 1009 proteins, and 1073 unique peptides were successfully identified. 51 differentially expressed proteins(DEPs) were identified(15 DEPs when M/C and 43 DEPs when Q/M; 7 DEPs in common). Classification and interaction network showed that these proteins participated in various biological processes including immune response, blood coagulation, complement activation, and so forth. Particularly, fibrinopeptide B(FpB) and heparin cofactor Ⅱ(HCII) were evaluated as important nodes in the interaction network, which was closely involved in coagulation and inflammation. Further, the Fp B level was increased in Group M but decreased in Group Q, while the HCII level exhibited the opposite trend. These findings not only indicated FpB and HCII as potential biomarkers and targets of QFOL in the treatment of RSV pneumonia, but also suggested a regulatory role of QFOL in the RSV-induced disturbance of coagulation and inflammation-coagulation interactions.

Plasma Metabolic Profile Determination in Young ST-segment Elevation Myocardial Infarction Patients with Ischemia and Reperfusion： Ultra-performance Liquid Chromatography and Mass Spectrometry for Pathway Analysis

Background： This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction （STEMI） youth patients experiencing ischemia and reperfusion via ultra-performance liquid chromatography and mass spectrometry （UPLC/MS） platform, which searches for closely related characteristic metabolites and metabolic pathways to evaluate their predictive value in the prognosis after discharge. Methods： Forty-seven consecutive STEMI patients （23 patients under 45 years of age, referred to here as ＂youth,＂ and 24 elderly patients） and 48 healthy control group members （24 youth, 24 elderly） were registered prospectively. The youth patients were required to provide a second blood draw during a follow-up visit one year after morbidity （n - 22, one lost）. Characteristic metabolites and relative metabolic pathways were screened via UPLC/MS platform base on the Kyoto encyclopedia of genes and genomes （KEGG） and Human Metabolome Database. Receiver operating characteristic （ROC） curves were drawn to evaluate the predictive value of characteristic metabolites in the prognosis after discharge. Results： We successfully established an orthogonal partial least squares discriminated analysis model （R2X = 71.2%, R2Y = 79.6%, and Q2 55.9%） and screened out 24 ions; the sphingolipid metabolism pathway showed the most drastic change. The ROC curve analysis showed that ceramide [Cer（dl 8：0/16：0）, Cer（t 18：0/12：0）] and sphinganine in the sphingolipid pathway have high sensitivity and specificity on the prognosis related to major adverse cardiovascular events after youth patients were discharged. The area under curve （AUC） was 0.67 1, 0.750, and 0.711, respectively. A follow-up validation one year after morbidity showed corresponding AUC of 0.778, 0.833, and 0.806. Conclusions： By analyzing the plasma metabolism of myocardial infarction patients, we successfully established a model that can distinguish two different factors simultaneously： pathological conditions and age. Sphingolipid metabolism is the top most altered pathway in young STEMI patients and as such may represent a valuable prognostic factor and potential therapeutic target.

A quick and effective multivariate statistical strategy for imaging mass spectrometry

A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry（IMS） techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS（AFAI-IMS）.The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis（PCA） and partial least square analysis（PLS）.It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.

Effects of antibiotic antitumor drugs on nucleotide levels in cultured tumor cells:an exploratory method to distinguish the mechanisms of antitumor drug action based on targeted metabolomics

Nucleotide pools in mammalian cells change due to the influence of antitumor drugs,which may help in evaluating the drug effect and understanding the mechanism of drug action.In this study,an ion-pair RP-HPLC method was used for a simple,sensitive and simultaneous determination of the levels of 12 nucleotides in mammalian cells treated with antibiotic antitumor drugs(daunorubicin,epirubicin and dactinomycin D).Through the use of this targeted metabolomics approach to find potential biomarkers,UTP and ATP were verified to be the most appropriate biomarkers.Moreover,a holistic statistical approach was put forward to develop a model which could distinguish 4 categories of drugs with different mechanisms of action.This model can be further validated by evaluating drugs with different mechanismsof action.This targeted metabolomics study may provide a novel approach to predict the mechanism of action of antitumor drugs.