Systematic review of fatty acid composition of human milk from mothers of preterm compared to full-term infants

Background: Fatty acid composition of human milk serves as guidance for the composition of infant formulae. The aim of the study was to systematically review data on the fatty acid composition of human milk of mothers of preterm compared to full-term infants. Methods: An electronic literature search was performed in English (Medline and Medscape) and German (SpringerLink) databases and via the Google utility. Fatty acid compositional data for preterm and fullterm human milk were converted to differences between means and 95% confidence intervals. Results: We identified five relevant studies publishing direct comparison of fatty acid composition of preterm versus full-term human milk. There were no significant differences between the values of the principal saturated and monounsaturated fatty acids. In three independent studies covering three different time points of lactation, however, docosahexaenoic acid (DHA) values were significantly higher in milk of mothers of preterm as compared to those of full-term infants, with an extent of difference considered nutritionally relevant. Conclusion: Higher DHA values in preterm than in full-term human milk underlines the importance of using own mother's milk for feeding preterm babies and raises the question whether DHA contents in preterm formulae should be higher than in formulae for full-term infants. Copyright (c) 2008 S. Karger AG, Basel

Exposure to Phthalates in Neonatal Intensive Care Unit Infants: Urinary Concentrations of Monoesters and Oxidative Metabolites

OBJECTIVE: We previously demonstrated that among 54 infants in neonatal intensive care units, exposure to polyvinyl chloride plastic medical devices containing the plasticizer di(2-ethylhexyl) phthalate (DEHP) is associated with urinary concentrations of mono(2-ethylhexyl) phthalate (MEHP), a DEHP metabolite. In this follow-up report, we studied the neonates’ exposure to DEHP-containing devices in relation to urinary concentrations of two other DEHP metabolites, and to urinary concentrations of metabolites of dibutyl phthalate (DBP) and benzylbutyl phthalate (BzBP), phthalates found in construction materials and personal care products. MEASUREMENTS: A priori, we classified the intensiveness of these 54 infants’ exposure to DEHP-containing medical products. We measured three metabolites of DEHP in infants’ urine: MEHP and two of its oxidative metabolites, mono(2-ethyl-5-hydroxylhexyl) phthalate (MEHHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP). We also measured monobutyl phthalate (MBP), a metabolite of DBP, and monobenzyl phthalate (MBzP), a metabolite of BzBP. RESULTS: Intensiveness of DEHP-containing product use was monotonically associated with all three DEHP metabolites. Urinary concentrations of MEHHP and MEOHP among infants in the high-DEHP-intensiveness group were 13–14 times the concentrations among infants in the low-intensiveness group (p ≤ 0.007). Concentrations of MBP were somewhat higher in the medium-and high-DEHP-intensiveness group; MBzP did not vary by product use group. Incorporating all phthalate data into a structural equation model confirmed the specific monotonic association between intensiveness of product use and biologic measures of DEHP. CONCLUSION: Inclusion of the oxidative metabolites MEHHP and MEOHP strengthened the association between intensiveness of product use and biologic indices of DEHP exposure over that observed with MEHP alone

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

VarySysDB: a human genetic polymorphism database based on all H-InvDB transcripts

Creation of a vast variety of proteins is accomplished by genetic variation and a variety of alternative splicing transcripts. Currently, however, the abundant available data on genetic variation and the transcriptome are stored independently and in a dispersed fashion. In order to provide a research resource regarding the effects of human genetic polymorphism on various transcripts, we developed VarySysDB, a genetic polymorphism database based on 187 156 extensively annotated matured mRNA transcripts from 36 073 loci provided by H-InvDB. VarySysDB offers information encompassing published human genetic polymorphisms for each of these transcripts separately. This allows comparisons of effects derived from a polymorphism on different transcripts. The published information we analyzed includes single nucleotide polymorphisms and deletion–insertion polymorphisms from dbSNP, copy number variations from Database of Genomic Variants, short tandem repeats and single amino acid repeats from H-InvDB and linkage disequilibrium regions from D-HaploDB. The information can be searched and retrieved by features, functions and effects of polymorphisms, as well as by keywords. VarySysDB combines two kinds of viewers, GBrowse and Sequence View, to facilitate understanding of the positional relationship among polymorphisms, genome, transcripts, loci and functional domains. We expect that VarySysDB will yield useful information on polymorphisms affecting gene expression and phenotypes. VarySysDB is available at http://h-invitational.jp/varygene/

The Degradome database: mammalian proteases and diseases of proteolysis

The degradome is defined as the complete set of proteases present in an organism. The recent availability of whole genomic sequences from multiple organisms has led us to predict the contents of the degradomes of several mammalian species. To ensure the fidelity of these predictions, our methods have included manual curation of individual sequences and, when necessary, direct cloning and sequencing experiments. The results of these studies in human, chimpanzee, mouse and rat have been incorporated into the Degradome database, which can be accessed through a web interface at http://degradome.uniovi.es. The annotations about each individual protease can be retrieved by browsing catalytic classes and families or by searching specific terms. This web site also provides detailed information about genetic diseases of proteolysis, a growing field of great importance for multiple users. Finally, the user can find additional information about protease structures, protease inhibitors, ancillary domains of proteases and differences between mammalian degradomes

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

The UniTrap resource: tools for the biologist enabling optimized use of gene trap clones

We have developed a comprehensive resource devoted to biologists wanting to optimize the use of gene trap clones in their experiments. We have processed 300 602 such clones from both public and private projects to generate 28 199 ‘UniTraps’, i.e. distinct collections of unambiguous insertions at the same subgenic region of annotated genes. The UniTrap resource contains data relative to 9583 trapped genes, which represent 42.3% of the mouse gene content. Among the trapped genes, 7 728 have a counterpart in humans, and 677 are known to be involved in the pathogenesis of human diseases. The aim of this analysis is to provide the wet lab researchers with a comprehensive database and curated tools for (i) identifying and comparing the clones carrying a trap into the genes of interest, (ii) evaluating the severity of the mutation to the protein function in each independent trapping event and (iii) supplying complete information to perform PCR, RT-PCR and restriction experiments to verify the clone and identify the exact point of vector insertion. To share this unique resource with the scientific community, we have designed and implemented a web interface that is freely accessible at http://unitrap.cbm.fvg.it/

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Specialized dynamical properties of promiscuous residues revealed by simulated conformational ensembles

The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein-protein interaction prediction and design methods. © 2013 American Chemical Society