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Menopause is an endocrine-related transition that induces a number of physiological and potentially pathological changes in middle-aged and elderly women. The intention of this research was to investigate the influence of menopause on the intricate relationships between major biochemical metabolites. The study involved metabolic profiling of 186 metabolic markers measured in blood plasma collected from 120 healthy female participants. We developed a method of network analysis using differential correlation that enabled us to detect and characterize differences in metabolites and changes in inter-relationships in pre- and post-menopausal women. A topological analysis was performed on the differential network that uncovered metabolite differences in pre-and post-menopausal women. In this analysis, our method identified two key metabolites, sphingomyelins and phosphatidylcholines, which may be useful in directing further studies into menopause-specific differences in the metabolome, and how these differences may underlie the body's response to stress and disease following the transition from pre- to post-menopausal status for women. [ABSTRACT FROM AUTHOR]

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females. [ABSTRACT FROM AUTHOR]

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts. [ABSTRACT FROM AUTHOR]

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease. [ABSTRACT FROM AUTHOR]

Hepatitis E virus (HEV) is responsible for hepatitis E, which represents a global public health problem. HEV genotypes 3 and 4 are reported to be zoonotic, and animals are monitored for HEV infection in the interests of public hygiene and food safety. The development of novel diagnostic methods and vaccines for HEV in humans is thus important topics of research. Opening reading frame (ORF) 2 of HEV includes both linear and conformational epitopes and is regarded as the primary candidate for vaccines and diagnostic tests. We investigated the precise location of the HEV epitopes in the ORF2 protein. We prepared four monoclonal antibodies (mAbs) against genotype 4 ORF2 protein and identified two linear epitopes, G438IVIPHD444 and Y457DNQH461, corresponding to two of these mAbs using phage display biopanning technology. Both these epitopes were speculated to be universal to genotypes 1, 2, 3, 4, and avian HEVs. We also used two 12-mer fragments of ORF2 protein including these two epitopes to develop a peptide-based enzyme-linked immunosorbent assay (ELISA) to detect HEV in serum. This assay demonstrated good specificity but low sensitivity compared with the commercial method, indicating that these two epitopes could serve as potential candidate targets for diagnosis. Overall, these results further our understanding of the epitope distribution of HEV ORF2, and provide important information for the development of peptide-based immunodiagnostic tests to detect HEV in serum. [ABSTRACT FROM AUTHOR]

Schistosomiasis is a neglected parasitic disease of major public health concern as it affects over 250 million people in developing countries. Currently there is no licensed vaccine available against schistosomiasis. The Schistosoma mansoni calpain protein, Sm-p80, is a leading vaccine candidate now ready to move to clinical trials. In order to better assess Sm-p80 vaccine immunogenicity; here we used a systems biology approach employing RNA-sequencing to identify gene signatures and epistatic interactions following Sm-p80 vaccination in mouse and baboon models that may predict vaccine efficacy. Recombinant Sm-p80 + CpG-oligodeoxynucleotide (ODN) vaccine formulation induced both cellular and humoral immunity genes with a predominant TH1 response as well as TH2 and TH17 gene signatures. Early gene responses and gene-network interactions in mice immunized with rSm-p80 + ODN appear to be initiated through TLR4 signaling. CSF genes, S100A alarmin genes and TNFRSF genes appear to be a signature of vaccine immunogenicity/efficacy as identified by their participation in gene network interactions in both mice and baboons. These gene families may provide a basis for predicting desirable outcomes for vaccines against schistosomiasis leading to a better understanding of the immune system response to vaccination. [ABSTRACT FROM AUTHOR]

Cooperation is pervasive and constitutes the core behavioral principle of human social life. Previous studies have revealed that mutual cooperation was reliably correlated with two reward-related brain regions, the ventral striatum and the orbitofrontal cortex. Using functional magnetic resonance imaging (fMRI), this study sought to investigate how the loss and gain contexts modulated the neural responses to mutual cooperation. Twenty-five female participants were scanned when they played a series of one-shot prisoner’s dilemma games in the loss and gain contexts. Specifically, participants and partners independently chose to either cooperate with each other or not, and each was awarded or deprived of (in the gain context or the loss context, respectively) a sum of money which depended upon the interaction of their choices. Behavioral results indicated that participants cooperated in nearly half of the experiment trials and reported higher level of positive emotions for mutual cooperation in both contexts, but they cooperated more in the gain than in the loss context. At the neural level, stronger activities in the orbitofrontal cortex were observed for mutual cooperation compared with the other three outcomes in both contexts, while stronger activation in ventral striatum associated with mutual cooperation was observed in the gain context only. Together, our data indicated that, even in the one-shot interaction under loss context, participants still exhibited preference for cooperation and the rewarding experience from a mutually cooperative social interaction activated the ventral striatum and the orbitofrontal cortex, but the loss context weakened the association between the ventral striatum activation and mutual cooperation. [ABSTRACT FROM AUTHOR]

To evaluate the effect of increased mouse density in a cage, mice were housed at the density recommended by the 1996 Guide for the Care and Use of Laboratory Animals and at densities that were approximately 2, 2.6, and 3 times greater. Five strains of mice (129S1/SvImJ, A/J, BALB/cByJ, C57BL/6J, and DBA/2J) were evaluated throughout 3- and 8-month timeframes for health and well-being, including mortality, cardiac measures, plasma cholesterol, body weight, bone mineral density, organ weights, hematology, behavioral observations, and open field and light–dark tests. For 22 of the 27 traits measured, increased housing density had no significant effect. Kidney weight, adrenal weight, and heart rate decreased as mice were housed more densely, and some of the decreases were statistically significant. Reduced kidney weight, adrenal weight, and heart rate are not considered to be negative outcomes and may even indicate reduced stress. However, all measurements of these three traits were within normal physiological ranges. Percent fat increased slightly in strains 129S1/SvImJ, A/J, and DBA/2J, but did not increase in strains BALB/cByJ, and C57BL/6J. These results indicate that mice can be housed at higher densities than those currently recommended. [ABSTRACT FROM AUTHOR]