Candidate imprinted transcriptional units in the mouse genome were identified systematically from 27,663 FANTOM2 full-length mouse cDNA clones by expression profiling. Large-scale cDNA microarrays were used to detect differential expression dependent upon chromosomal parent of origin by comparing the mRNA levels in the total tissue of 9.5 dpc parthenogenote and androgenote mouse embryos. Of the FANTOM2 transcripts, 2114 were identified as candidates on the basis of the array data. Of these, 39 mapped to known imprinted regions of the mouse genome, 56 were considered as nonprotein-coding RNAs, and 159 were natural antisense transcripts. The imprinted expression of two transcripts located in the mouse chromosomal region syntenic to the human Prader-Willi syndrome region was confirmed experimentally. We further mapped all candidate imprinted transcripts to the mouse and human genome and were shown in correlation with the imprinting disease loci. These data provide a major resource for understanding the role of imprinting in mammalian inherited traits.

Molecular analyses of lung aspirates from Gambian children with severe pneumonia detected pathogens more frequently than did culture and showed a predominance of bacteria, principally Streptococcus pneumoniae, >75% being of serotypes covered by current pneumococcal conjugate vaccines. Multiple pathogens were detected frequently, notably Haemophilus influenzae (mostly nontypeable) together with S. pneumoniae.

Conventional approaches to target labelling for expression microarray analysis typically require relatively large amounts of total RNA, a serious limitation when the sample available is small. Here we explore the cycle-dependent ampli®cation characteristics of Template-Switching PCR and validate its use for microarray target labelling. TS-PCR iden-ti®es up to 80% of the differentially expressed genes identi®ed by direct labelling using 30-fold less input RNA for the ampli®cation, with the equivalent of 1000-fold less starting material being used for each hybridisation. Moreover, the sensitivity of microarray experiments is increased considerably, allowing the identi®cation of differentially expressed transcripts below the level of detection using targets prepared by direct labelling. We have also validated the ®delity of ampli®cation and show that the ampli®ed material faithfully represents the starting mRNA population. This method outperforms conventional labelling strategies, not only in terms of sensitivity and the identi®cation of differentially expressed genes, but it is also faster and less labour intensive than other ampli®cation protocols.

BackgroundPneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.Methodology/Principal FindingsEleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). ‘Unsupervised’ (blinded) data were analyzed by Principal Component Analysis (PCA), while ‘supervised’ (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5′-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.Conclusions/SignificanceMetabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.