Breastfeeding Success among Infants with Phenylketonuria

Breast milk is the nutrition of choice for human infants (American Academy of Pediatrics, 2005; American Association of Family Physicians, 2008; Association of Women’s Health Obstetric and Neonatal Nurses, 2005; Canadian Paediatric Society, 2005; U.S. Preventive Services Task Force, 2008; World Health Organization, 2009). The literature on the benefits of breast milk and breastfeeding for infants and mothers has established multiple positive outcomes for infants (Hoddinott, Tappin, & Wright, 2008; Horta, Bahl, Martines, & Victora, 2007; Ip et al., 2007). Breast milk has advantages for infants that distinguish it from standard commercial infant formulas. These advantages include growth factors, hormones, immunological factors, and long-chain polyunsaturated fatty acids. For infants with phenylketonuria (PKU), breast milk has additional advantages over any standard commercial infant formula, such as a lower concentration of protein and a lower content of the amino acid, phenylalanine. Despite these benefits, some clinics encourage mothers of infants with PKU to breastfeed whereas others present breastfeeding as an unacceptable option. Although the possible risks and benefits of breastfeeding infants with PKU have been discussed, there is limited research and practice describing breastfeeding infants with PKU. As a result, breastfeeding infants with PKU is based more upon limited clinical experiences rather than upon evidence based practice that aims to apply the best scientific evidence gained from research to clinical decision making

Genes to Diseases (G2D) Computational Method to Identify Asthma Candidate Genes

Asthma is a complex trait for which different strategies have been used to identify its environmental and genetic predisposing factors. Here, we describe a novel methodological approach to select candidate genes for asthma genetic association studies. In this regard, the Genes to Diseases (G2D) computational tool has been used in combination with a genome-wide scan performed in a sub-sample of the Saguenay−Lac-St-Jean (SLSJ) asthmatic familial collection (n = 609) to identify candidate genes located in two suggestive loci shown to be linked with asthma (6q26) and atopy (10q26.3), and presenting differential parent-of-origin effects. This approach combined gene selection based on the G2D data mining analysis of the bibliographic and protein public databases, or according to the genes already known to be associated with the same or a similar phenotype. Ten genes (LPA, NOX3, SNX9, VIL2, VIP, ADAM8, DOCK1, FANK1, GPR123 and PTPRE) were selected for a subsequent association study performed in a large SLSJ sample (n = 1167) of individuals tested for asthma and atopy related phenotypes. Single nucleotide polymorphisms (n = 91) within the candidate genes were genotyped and analysed using a family-based association test. The results suggest a protective association to allergic asthma for PTPRE rs7081735 in the SLSJ sample (p = 0.000463; corrected p = 0.0478). This association has not been replicated in the Childhood Asthma Management Program (CAMP) cohort. Sequencing of the regions around rs7081735 revealed additional polymorphisms, but additional genotyping did not yield new associations. These results demonstrate that the G2D tool can be useful in the selection of candidate genes located in chromosomal regions linked to a complex trait

Phenylketonuria in Portugal: Genotype-Phenotype Correlations Using Molecular, Biochemical, and Haplotypic Analyses

The impairment of the hepatic enzyme phenylalanine hydroxylase (PAH) causes elevation of phenylalanine levels in blood and other body fluids resulting in the most common inborn error of amino acid metabolism (phenylketonuria). Persistently high levels of phenylalanine lead to irreversible damage to the nervous system. Therefore, early diagnosis of the affected individuals is important, as it can prevent clinical manifestations of the disease.info:eu-repo/semantics/publishedVersio

Haplotype analysis of TP53 polymorphisms, Arg72Pro and Ins16, in BRCA1 and BRCA2 mutation carriers of French Canadian descent

Research articl

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download