Incorporation of Knowledge for Network-based Candidate Gene Prioritization

In order to identify the genes associated with a given disease, a number of different high-throughput techniques are available such as gene expression profiles. However, these high-throughput approaches often result in hundreds of different candidate genes, and it is thus very difficult for biomedical researchers to narrow their focus to a few candidate genes when studying a given disease. In order to assist in this challenge, a process called gene prioritization can be utilized. Gene prioritization is the process of identifying and ranking new genes as being associated with a given disease. Candidate genes which rank high are deemed more likely to be associated with the disease than those that rank low. This dissertation focuses on a specific kind of gene prioritization method called network-based gene prioritization. Network-based methods utilize a biological network such as a protein-protein interaction network to rank the candidate genes. In a biological network, a node represents a protein (or gene), and a link represents a biological relationship between two proteins such as a physical interaction. The purpose of this dissertation was to investigate if the incorporation of biological knowledge into the network-based gene prioritization process can provide a significant benefit. The biological knowledge consisted of a variety of information about a given gene including gene ontology (GO) functional terms, MEDLINE articles, gene co-expression measurements, and protein domains to name just a few. The biological knowledge was incorporated into the network’s links and nodes as link and node knowledge respectively. An example of link knowledge is the degree of functional similarity between two proteins, and an example of node knowledge is the number of GO terms associated with a given protein. Since there were no existing network-based inference algorithms which could incorporate node knowledge, I developed a new network-based inference algorithm to incorporate both link and node knowledge called the Knowledge Network Gene Prioritization (KNGP) algorithm. The results showed that the incorporation of biological knowledge via link and node knowledge can provide a significant benefit for network-based gene prioritization. The KNGP algorithm was utilized to combine the link and node knowledge

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links

Background: Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes. Results: We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone. Conclusions: The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization. © 2013 Kimmel, Visweswaran

CSAP localizes to polyglutamylated microtubules and promotes proper cilia function and zebrafish development

The diverse populations of microtubule polymers in cells are functionally distinguished by different posttranslational modifications, including polyglutamylation. Polyglutamylation is enriched on subsets of microtubules including those found in the centrioles, mitotic spindle, and cilia. However, whether this modification alters intrinsic microtubule dynamics or affects extrinsic associations with specific interacting partners remains to be determined. Here we identify the microtubule-binding protein centriole and spindle–associated protein (CSAP), which colocalizes with polyglutamylated tubulin to centrioles, spindle microtubules, and cilia in human tissue culture cells. Reducing tubulin polyglutamylation prevents CSAP localization to both spindle and cilia microtubules. In zebrafish, CSAP is required for normal brain development and proper left–right asymmetry, defects that are qualitatively similar to those reported previously for depletion of polyglutamylation-conjugating enzymes. We also find that CSAP is required for proper cilia beating. Our work supports a model in which polyglutamylation can target selected microtubule-associated proteins, such as CSAP, to microtubule subpopulations, providing specific functional capabilities to these populations.National Institutes of Health (U.S.) (Grant no. GM074746)American Cancer Society. Research Scholar Grant (121776)National Institute of General Medical Sciences (U.S.) (GM088313

International Society of Sports Nutrition Position Stand: Probiotics.

Position statement: The International Society of Sports Nutrition (ISSN) provides an objective and critical review of the mechanisms and use of probiotic supplementation to optimize the health, performance, and recovery of athletes. Based on the current available literature, the conclusions of the ISSN are as follows: 1)Probiotics are live microorganisms that, when administered in adequate amounts, confer a health benefit on the host (FAO/WHO).2)Probiotic administration has been linked to a multitude of health benefits, with gut and immune health being the most researched applications.3)Despite the existence of shared, core mechanisms for probiotic function, health benefits of probiotics are strain- and dose-dependent.4)Athletes have varying gut microbiota compositions that appear to reflect the activity level of the host in comparison to sedentary people, with the differences linked primarily to the volume of exercise and amount of protein consumption. Whether differences in gut microbiota composition affect probiotic efficacy is unknown.5)The main function of the gut is to digest food and absorb nutrients. In athletic populations, certain probiotics strains can increase absorption of key nutrients such as amino acids from protein, and affect the pharmacology and physiological properties of multiple food components.6)Immune depression in athletes worsens with excessive training load, psychological stress, disturbed sleep, and environmental extremes, all of which can contribute to an increased risk of respiratory tract infections. In certain situations, including exposure to crowds, foreign travel and poor hygiene at home, and training or competition venues, athletes' exposure to pathogens may be elevated leading to increased rates of infections. Approximately 70% of the immune system is located in the gut and probiotic supplementation has been shown to promote a healthy immune response. In an athletic population, specific probiotic strains can reduce the number of episodes, severity and duration of upper respiratory tract infections.7)Intense, prolonged exercise, especially in the heat, has been shown to increase gut permeability which potentially can result in systemic toxemia. Specific probiotic strains can improve the integrity of the gut-barrier function in athletes.8)Administration of selected anti-inflammatory probiotic strains have been linked to improved recovery from muscle-damaging exercise.9)The minimal effective dose and method of administration (potency per serving, single vs. split dose, delivery form) of a specific probiotic strain depends on validation studies for this particular strain. Products that contain probiotics must include the genus, species, and strain of each live microorganism on its label as well as the total estimated quantity of each probiotic strain at the end of the product's shelf life, as measured by colony forming units (CFU) or live cells.10)Preclinical and early human research has shown potential probiotic benefits relevant to an athletic population that include improved body composition and lean body mass, normalizing age-related declines in testosterone levels, reductions in cortisol levels indicating improved responses to a physical or mental stressor, reduction of exercise-induced lactate, and increased neurotransmitter synthesis, cognition and mood. However, these potential benefits require validation in more rigorous human studies and in an athletic population

The components, inputs and output of KNGP algorithm.

<p>The components, inputs and output of KNGP algorithm.</p

Specification of node weights for each group in the synthetic networks.

<p>Specification of node weights for each group in the synthetic networks.</p

Evaluation protocol.

<p>Evaluation protocol.</p

Pseudocode for the KNGP algorithm.

<p>Pseudocode for the KNGP algorithm.</p

AUCs for various values of <i>f</i> for the four synthetic datasets.

<p>AUCs for various values of <i>f</i> for the four synthetic datasets.</p