Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.
Professor of Computational Biology and Bioinformatics, Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute (Watch Webinar "Integrative Systems Approaches to Network Modeling of Biological Processes" by John Quackenbush,...
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The ISME Journal: Multidisciplinary Journal of Microbial Ecology is the official Journal of the International Society for Microbial Ecology, publishing high-quality, original research papers, short communications, commentary articles and reviews in the rapidly expanding and diverse discipline of microbial ecology.
IN the late 17th century, the Dutch naturalist Anton van Leeuwenhoek looked at his own dental plaque through a microscope and saw a world of tiny cells “very prettily a-moving.” He could not have predicted that a few centuries later, the trillions of microbes that share our lives — collectively known as the microbiome — would rank among the hottest areas of biology.
These microscopic partners help us by digesting our food, training our immune systems and crowding out other harmful microbes that could cause disease. In return, everything from the food we eat to the medicines we take can shape our microbial communities — with important implications for our health. Studies have found that changes in our microbiome accompany medical problems from obesity to diabetes to colon cancer.
Immediately following birth, the gastrointestinal tract is colonized with a complex community of bacteria, which helps shape the immune system. Under conditions of health, the immune system is able to differentiate between innocuous antigens, including food protein and commensals, and harmful antigens such as pathogens. However, patients with celiac disease (CD) develop an intolerance to gluten proteins which results in a pro-inflammatory T-cell mediated immune response with production of anti-g
Alcohol dependence has traditionally been considered a brain disorder. Alteration in the composition of the gut microbiota has recently been shown to be present in psychiatric disorders, which suggests the possibility of gut-to-brain interactions in the development of alcohol dependence. The aim of the present study was to explore whether changes in gut permeability are linked to gut-microbiota composition and activity in alcohol-dependent subjects. We also investigated whether gut dysfunction is associated with the psychological symptoms of alcohol dependence. Finally, we tested the reversibility of the biological and behavioral parameters after a short-term detoxification program. We found that some, but not all, alcohol-dependent subjects developed gut leakiness, which was associated with higher scores of depression, anxiety, and alcohol craving after 3 wk of abstinence, which may be important psychological factors of relapse. Moreover, subjects with increased gut permeability also had altered composition and activity of the gut microbiota. These results suggest the existence of a gut–brain axis in alcohol dependence, which implicates the gut microbiota as an actor in the gut barrier and in behavioral disorders. Thus, the gut microbiota seems to be a previously unidentified target in the management of alcohol dependence.
A new phylogenetics approach and algorithm with which to chart the evolutionary history of organisms is presented. It utilizes mass spectral data produced from the proteolytic digestion of proteins, rather than partial or complete gene or translated gene sequences. The concept and validity of the approach is demonstrated herein using both theoretical and experimental mass data, together with the translated gene sequences of the hemagglutinin protein of the influenza virus. A comparison of the mass trees with conventional sequenced-based phylogenetic trees, using two separate tree comparison algorithms, reveals a high degree of similarity and congruence among the trees. Given that the mass map data can be generated more rapidly than gene sequences, even when next generation parallel sequencing is employed, mass trees offer new opportunities and advantages for phylogenetic analysis.
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