Bioinformatics tools and databases for the study of human growth hormone

Advances in novel molecular biological diagnostic methods are changing the way of diagnosis and study of metabolic disorders like growth hormone deficiency. Faster sequencing and genotyping methods require strong bioinformatics tools to make sense of the vast amount of data generated by modern laboratories. Advances in genome sequencing and computational power to analyze the whole genome sequences will guide the diagnostics of future. In this chapter, an overview of some basic bioinformatics resources that are needed to study metabolic disorders are reviewed and some examples of bioinformatics analysis of human growth hormone gene, protein and structure are provided.

Characterization of the potential role for RNA-binding protein FUS in DNA damage response: A quantitative proteomic approach

FUS/TLS (fused in sarcoma/translocated in liposarcoma) is a ubiquitously expressed protein of the hnRNP family, that has been discovered as fused to transcription factors in several human sarcomas and found in protein aggregates in neurons of patients with an inherited form of Amyotrophic Lateral Sclerosis [Vance C. et al., 2009]. FUS is a 53 kDa nuclear protein that contains structural domains, such as a RNA Recognition Motif (RRM) and a zinc finger motif, that give to FUS the ability to bind to both RNA and DNA sequences. It has been implicated in a variety of cellular processes, such as pre-mRNA splicing, miRNA processing, gene expression control and transcriptional regulation [Fiesel FC. and Kahle PJ., 2011]. Moreover, some evidences link FUS to genome stability control and DNA damage response: mice lacking FUS are hypersensitive to ionizing radiation (IR) and show high levels of chromosome instability and, in response to double-strand breaks, FUS is phosphorylated by the protein kinase ATM [Kuroda M. et al., 2000; Hicks GG. et al., 2000; Gardiner M. et al., 2008]. Furthermore, preliminary results of mass spectrometric identification of FUS interacting proteins in HEK293 cells, expressing a recombinant flag-tagged FUS protein, highlighted the interactions with proteins involved in DNA damage response, such as DNA-PK, XRCC-5/-6, and ERCC-6, raising the possibilities that FUS is involved in this pathway, even though its role still needs to be clarified. This study aims to investigate the biological roles of FUS in human cells and in particular the putative role in DNA damage response through the characterization of the proteomic profile of the neuroblastoma cell line SH-SY5Y upon FUS inducible depletion, by a quantitative proteomic approach. The SH-SY5Y cell line that will be used in this study expresses, in presence of tetracycline, a shRNA that targets FUS mRNA, leading to FUS protein depletion (SH-SY5Y FUS iKD cells). To quantify changes in proteins expression levels a SILAC strategy (Stable Isotope Labeling by Amino acids in Cell culture) will be conducted on SH-SY5Y FUS iKD cells and a control SH-SY5Y cell line (that expresses a mock shRNA) and the relative changes in proteins levels will be evaluated after five and seven days upon FUS depletion, by nanoliquid chromatography coupled to tandem mass spectrometry (nLC-MS/MS) and bioinformatics analysis. Preliminary experiments demonstrated that the SH-SY5Y FUS iKD cells, when subjected to genotoxic stress (high dose of IR), upon inducible depletion of FUS, showed a increased phosphorylation of gH2AX with respect to control cells, suggesting an higher activation of the DNA damage response.

Whole-exome sequencing reveals a C-terminal germline variant in CEBPA-associated acute myeloid leukemia: 45-year follow-up of a large family.

Familial acute myeloid leukemia is rare and linked to germline mutations in RUNX1, GATA2 or CCAAT/enhancer binding protein-α (CEBPA). We re-evaluated a large family with acute myeloid leukemia originally seen at NIH in 1969. We utilized whole-exome sequencing to study this family, and conducted in silico bioinformatics analysis, protein structural modeling and laboratory experiments to assess the impact of the identified CEBPA Q311P mutation. Unlike most previously identified germline mutations in CEBPA, which were N-terminal frameshift mutations, we identified a novel Q311P variant that was located in the C-terminal bZip domain of C/EBPα. Protein structural modeling suggested that the Q311P mutation alters the ability of the CEBPA dimer to bind DNA. Electrophoretic mobility shift assays showed that the Q311P mutant had attenuated binding to DNA, as predicted by the protein modeling. Consistent with these findings, we found that the Q311P mutation has reduced transactivation, consistent with a loss-of-function mutation. From 45 years of follow-up, we observed incomplete penetrance (46%) of CEBPA Q311P. This study of a large multi-generational pedigree reveals that a germline mutation in the C-terminal bZip domain can alter the ability of C/EBP-α to bind DNA and reduces transactivation, leading to acute myeloid leukemia.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Combining spatial-temporal and phylogenetic analysis approaches for improved understanding on global H5N1 transmission

Background Since late 2003, the highly pathogenic influenza A H5N1 had initiated several outbreak waves that swept across the Eurasia and Africa continents. Getting prepared for reassortment or mutation of H5N1 viruses has become a global priority. Although the spreading mechanism of H5N1 has been studied from different perspectives, its main transmission agents and spread route problems remain unsolved. Methodology/Principal Findings Based on a compilation of the time and location of global H5N1 outbreaks from November 2003 to December 2006, we report an interdisciplinary effort that combines the geospatial informatics approach with a bioinformatics approach to form an improved understanding on the transmission mechanisms of H5N1 virus. Through a spherical coordinate based analysis, which is not conventionally done in geographical analyses, we reveal obvious spatial and temporal clusters of global H5N1 cases on different scales, which we consider to be associated with two different transmission modes of H5N1 viruses. Then through an interdisciplinary study of both geographic and phylogenetic analysis, we obtain a H5N1 spreading route map. Our results provide insight on competing hypotheses as to which avian hosts are responsible for the spread of H5N1. Conclusions/Significance We found that although South China and Southeast Asia may be the virus pool of avian flu, East Siberia may be the source of the H5N1 epidemic. The concentration of migratory birds from different places increases the possibility of gene mutation. Special attention should be paid to East Siberia, Middle Siberia and South China for improved surveillance of H5N1 viruses and monitoring of migratory birds.

Comparison of genomic and proteomic data in recurrent airway obstruction affected horses using Ingenuity Pathway Analysis(R)

BACKGROUND: Recurrent airway obstruction (RAO) is a severe chronic respiratory disease affecting horses worldwide, though mostly in the Northern hemisphere. Environmental as well as genetic factors strongly influence the course and prognosis of the disease. Research has been focused on characterization of immunologic factors contributing to inflammatory responses, on genetic linkage analysis, and, more recently, on proteomic analysis of airway secretions from affected horses. The goal of this study was to investigate the interactions between eight candidate genes previously identified in a genetic linkage study and proteins expressed in bronchoalveolar lavage fluid (BALF) collected from healthy and RAO-affected horses. The analysis was carried out with Ingenuity Pathway Analysis(R) bioinformatics software. RESULTS: The gene with the greatest number of indirect interactions with the set of proteins identified is Interleukin 4 Receptor (IL-4R), whose protein has also been detected in BALF. Interleukin 21 receptor and chemokine (C-C motif) ligand 24 also showed a large number of interactions with the group of detected proteins. Protein products of other genes like that of SOCS5, revealed direct interactions with the IL-4R protein. The interacting proteins NOD2, RPS6KA5 and FOXP3 found in several pathways are reported regulators of the NFkappaB pathway. CONCLUSIONS: The pathways generated with IL-4R highlight possible important intracellular signaling cascades implicating, for instance, NFkappaB. Furthermore, the proposed interaction between SOCS5 and IL-4R could explain how different genes can lead to identical clinical RAO phenotypes, as observed in two Swiss Warmblood half sibling families because these proteins interact upstream of an important cascade where they may act as a functional unit.

SNA--a toolbox for the stoichiometric analysis of metabolic networks

BACKGROUND: Despite recent algorithmic and conceptual progress, the stoichiometric network analysis of large metabolic models remains a computationally challenging problem. RESULTS: SNA is a interactive, high performance toolbox for analysing the possible steady state behaviour of metabolic networks by computing the generating and elementary vectors of their flux and conversions cones. It also supports analysing the steady states by linear programming. The toolbox is implemented mainly in Mathematica and returns numerically exact results. It is available under an open source license from: http://bioinformatics.org/project/?group_id=546. CONCLUSION: Thanks to its performance and modular design, SNA is demonstrably useful in analysing genome scale metabolic networks. Further, the integration into Mathematica provides a very flexible environment for the subsequent analysis and interpretation of the results.

The Mycoplasma conjunctivae genome sequencing, annotation and analysis

BACKGROUND: The mollicute Mycoplasma conjunctivae is the etiological agent leading to infectious keratoconjunctivitis (IKC) in domestic sheep and wild caprinae. Although this pathogen is relatively benign for domestic animals treated by antibiotics, it can lead wild animals to blindness and death. This is a major cause of death in the protected species in the Alps (e.g., Capra ibex, Rupicapra rupicapra). METHODS: The genome was sequenced using a combined technique of GS-FLX (454) and Sanger sequencing, and annotated by an automatic pipeline that we designed using several tools interconnected via PERL scripts. The resulting annotations are stored in a MySQL database. RESULTS: The annotated sequence is deposited in the EMBL database (FM864216) and uploaded into the mollicutes database MolliGen http://cbi.labri.fr/outils/molligen/ allowing for comparative genomics. CONCLUSION: We show that our automatic pipeline allows for annotating a complete mycoplasma genome and present several examples of analysis in search for biological targets (e.g., pathogenic proteins).