Meaningful Biological Insights Research Articles

Unraveling the brain regulation of appetite: lessons from genetics

Over the past 20 years, genetic studies have illuminated critical pathways in the hypothalamus and brainstem mediating energy homeostasis, such as the melanocortin, leptin, 5-hydroxytryptamine and brain-derived neurotrophic factor signaling axes. The identification of these pathways necessary for appropriate appetitive responses to energy state has yielded insight into normal homeostatic processes. Although monogenic alterations in each of these axes result in severe obesity, such cases remain rare. The major burden of disease is carried by those with common obesity, which has so far resisted yielding meaningful biological insights. Recent progress into the etiology of common obesity has been made with genome-wide association studies. Such studies now reveal more than 32 different candidate obesity genes, most of which are highly expressed or known to act in the CNS, emphasizing, as in rare monogenic forms of obesity, the role of the brain in predisposition to obesity.

RESQUE: Network reduction using semi-Markov random walk scores for efficient querying of biological networks

Recent technological advances in measuring molecular interactions have resulted in an increasing number of large-scale biological networks. Translation of these enormous network data into meaningful biological insights requires efficient computational techniques that can unearth the biological information that is encoded in the networks. One such example is network querying, which aims to identify similar subnetwork regions in a large target network that are similar to a given query network. Network querying tools can be used to identify novel biological pathways that are homologous to known pathways, thereby enabling knowledge transfer across different organisms. In this article, we introduce an efficient algorithm for querying large-scale biological networks, called RESQUE. The proposed algorithm adopts a semi-Markov random walk (SMRW) model to probabilistically estimate the correspondence scores between nodes that belong to different networks. The target network is iteratively reduced based on the estimated correspondence scores, which are also iteratively re-estimated to improve accuracy until the best matching subnetwork emerges. We demonstrate that the proposed network querying scheme is computationally efficient, can handle any network query with an arbitrary topology and yields accurate querying results. The source code of RESQUE is freely available at http://www.ece.tamu.edu/~bjyoon/RESQUE/

Abstract 52: A new method to interpret gene expression microarray data for individual tumors by profile alignment against a reference dataset of 10,000 samples

Abstract Transforming genome wide expression profiles into meaningful biological insights is a complex problem, particularly for individual tumor samples. Numerous data-analysis methods have been developed for this purpose, but the vast majority of these aim at the identification of large subgroups of tumors based on patterns of similarity in gene expression within the investigator's own dataset. There are now several large microarray databases (e.g. GEO, Array-express, Oncomine, GeneSapiens) and meta-analysis projects that could be helpful in the interpretation of single microarray data. Our aim was to develop a microarray profile alignment method that would serve the exploration of transcriptomics data in analogy to how “BLAST” analysis is routinely performed for nucleotide sequence comparisons. Here, we describe a tool to facilitate the interpretation and exploration of microarray data from individual tumors with the help of the large uniform reference database, Gensapiens (www.genesapiens.org). The tool enables us to compare a single sample with any tissue or tumor type in the database, and determine the similarity between them. A leave-one-out cross validation test with the Genesapiens database samples achieved 93.6% classification accuracy for 47 normal tissue types and 87.5% for cancer tissue of origin, among 57 different cancer types. The latter result suggests application of the method in the diagnosis of unknown primary tumors (CUPS). We applied the method to a set of metastasis samples and achieved an overall accuracy of 75%, which could further be refined to 88% by discarding uncertain results, that is, leaving out the samples that have no significant similarity to any cancer. The similarity of the test sample to a tissue or cancer type can be further explored at the level of individual genes, gene sets, gene ontology subsets or pathways. As an example of personalized medicine applications, we aligned datasets from two external metastatic (liver and lung) samples and correctly identified both cases as colorectal cancer. However, 3-4 % of the genes had expression levels rarely seen in colorectal cancer, highlighting unique features and potential individual biomarkers to these two tumors. In conclusion, we have developed a gene expression microarray profile alignment tool that provides a new opportunity for the interpretation of microarray data from individual tumor samples against a large reference database, such as GeneSapiens. This could be helpful in i) independent validation of the tumor type ii) subclassification of the sample in relation to a large reference dataset, iii) exploring unique features of the tumor and iv) guiding personalized biomarker discovery and therapy. Thus, this provides a new paradigm for the analysis of microarray data and an ability to uncover biological insights from individual gene expression profiles. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 52. doi:10.1158/1538-7445.AM2011-52

FTO Biology and Obesity: Why Do a Billion of Us Weigh 3 kg More?

Few would dispute that the current obesity epidemic has been driven by lifestyle and environmental changes. However, it is clear that individuals respond differently to these “obesigenic” changes and this variation in response has a strong genetic element. Genome-wide association studies have revealed that single nucleotide polymorphisms in Fat mass and obesity-associated transcript (FTO) are robustly associated with body mass index and obesity. Although the effect of these risk alleles are modest, with heterozygous and homozygous carriers weighing approximately 1.5 and 3 kg more respectively, there are an estimated one billion homozygous carriers in the world, spanning multiple different ethnicities and populations. Yet despite its broad impact, the biological function of FTO, particularly its role in controlling energy balance, remains unknown. Although the study of severe Mendelian obesity has been invaluable in illuminating critical pathways controlling food intake, the major burden of disease is carried by those of us with “common obesity,” which to date has resisted yielding meaningful biological insights. FTO has at last given us a handle on a huge, worldwide, common problem. In this review, we focus on the available genetic and in vivo evidence to date that implicates FTO in the control of energy balance.

PathCluster: a framework for gene set-based hierarchical clustering

Motivation: Gene clustering and gene set-based functional analysis are widely used for the analysis of expression profiles. The development of a comprehensive method jointly combining the two methods would allow for greater biological insights.Results: We developed a software package, PathCluster for gene set-based clustering via an agglomerative hierarchical clustering algorithm. The distances between predefined gene sets are illustrated in a dendrogram in which the relationships between gene sets can be visually assessed. Valuable biological insights can be obtained according to the type of gene sets, e.g. coordinated action of molecular functions (functional gene sets) and putative motif synergy (promoter gene set) in a biological process. The combined use of gene sets further enables the interrogation of different biological themes and their putative relationships, such as function-versus-regulatory motif or drug-versus-function. PathCluster can also be used for knowledge-based sample partitioning or class categorization for clinical purposes. With extended applicability, PathCluster will facilitate the gleaning of meaningful biological insights and testable hypotheses in the contexts of given expression profiles.Availability: PathCluster executable files can be freely downloaded at http://www.systemsbiology.co.kr/PathCluster/.Contact: yejun@catholic.ac.kr

Evolutionary Ecology of Bacterial Viruses

Host-phage interactions provide a tractable model system for linking data and theory at multiple scales and could help in developing a new theoretical foundation for microbial ecology. This relatively simple system could prove to be a benchmark for microbiologists who are probing more complex cellular and ecosystem processes with increasingly finer resolution, producing an expanding surplus of data through a varied means of powerful experimental techniques. Recently, applications of mathematical concepts to microbiology have found successes in efforts to organize and reduce the complexity of high-throughput data. Of course, relying on quantitative methods to parse data cannot replace the hard thinking and additional theoretical tools needed to uncover meaningful biological insights.

Inferring cellular networks using probabilistic graphical models.

High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.