Institute for Systems Biology

About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.

Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software.

Abstract: Tandem mass spectrometry (MS/MS) allows for the rapid identification of many types of post-translational modifications (PTMs), especially those that can be detected by a diagnostic mass shift in one or more peptide fragment ions (for example, phosphorylation). But some PTMs (for example, SUMOs and other ubiquitin-like modifiers) themselves produce multiple fragment ions; combined with fragments from the modified target peptide, a complex overlapping fragmentation pattern is thus generated, which is uninterpretable by standard peptide sequencing software. Here we introduce SUMmOn, an automated pattern recognition tool that detects diagnostic PTM fragment ion series within complex MS/MS spectra, to identify modified peptides and modification sites within these peptides. Using SUMmOn, we demonstrate for the first time that human SUMO-1 multimerizes in vitro primarily via three N-terminal lysines, Lys7, Lys16 and Lys17. Notably, our method is theoretically applicable to any type of modification or chemical moiety generating a unique fragment ion pattern.

A CD133-related gene expression signature identifies an aggressive glioblastoma subtype with excessive mutations

Abstract: Cancer cells are heterogeneous and, it has been proposed, fall into at least two classes: the tumor-initiating cancer stem cells (CSC) and the more differentiated tumor cells. The transmembrane protein CD133 has been widely used to isolate putative CSC populations in several cancer types, but its validity as a CSC marker and hence its clinical ramifications remain controversial. Here, we conducted transcriptomic profiling of sorted CD133+ and CD133− cells from human glioblastoma multiforme (GBM) and, by subtractive analysis, established a CD133 gene expression signature composed of 214 differentially expressed genes. Extensive computational comparisons with a compendium of published gene expression profiles reveal that the CD133 gene signature transcriptionally resembles human ES cells and in vitro cultured GBM stem cells, and this signature successfully distinguishes GBM from lower-grade gliomas. More importantly, the CD133 gene signature identifies an aggressive subtype of GBM seen in younger patients with shorter survival who bear excessive genomic mutations as surveyed through the Cancer Genome Atlas Network GBM mutation spectrum. Furthermore, the CD133 gene signature distinguishes higher-grade breast and bladder cancers from their lower-grade counterparts. Our systematic analysis provides molecular and genetic support for the stem cell-like nature of CD133+ cells and an objective means for evaluating cancer aggressiveness.

Genetics. Systems genetics.

Abstract: From studies with peas over 150 years ago, Gregor Mendel deduced the laws that govern the inheritance of traits in most organisms. The brilliance, but also the limitation, of Mendel’s work was its focus on single-gene traits, such as flower color and plant height. However, phenotypic variation, including that which underlies health and disease in humans, often results from multiple interactions among numerous genetic and environmental factors. Systems genetics seeks to understand this complexity by integrating the questions and methods of systems biology with those of genetics to solve the fundamental problem of interrelating genotype and phenotype in complex traits and disease. This global perspective is possible because of the technologies, information, and infrastructure that derive from the Human Genome Project, which sequenced the genome as a way to locate genes and other functional DNA sequence elements. These advances now allow comprehensive “-omic” measurements of RNAs, proteins, small molecules, and chemical modifications of DNA. The application of these technologies has enabled an unprecedented scale and scope of genetic and phenotypic surveys. But this does not in itself constitute a new field of study. Instead, the defining principle of systems genetics is understanding how genetic information is integrated, coordinated, and ultimately transmitted through molecular, cellular, and physiological networks to enable the higher-order functions and emergent properties of biological systems. In contrast to the networks of molecular and physical interactions that dominate the field of systems biology, systems genetics focuses on networks of interactions between genes and traits, as well as between traits themselves. The analytical foundations for characterizing these relationships are based on graph theory and the statistics of correlation and causality (1, 2). Predictions that result from these network models can be tested with genetic mutations, chemical agents, or environmental exposures as single-factor perturbations. Machine learning methods (3) can prioritize candidate genes and network functions for further study. Typically, gene expression levels based on global profiles have been analyzed as quantitative phenotypes, so-called eQTLs (expression quantitative trait loci) (4) to study diverse biological phenomena in yeasts, plants, flies, worms, mice, and humans. Examples range from sleep patterns in flies (5) to metabolite concentrations in plants (6). To date, however, most of the pioneering studies in this field have focused on classically reductionist questions, such as gene discovery, rather than systems problems, such as homeostasis. An essential but not yet fully exploited application of systems genetics is the inference of higher-order functionality in complex systems from patterns of covariation among underlying molecular and physiological phenotypes. A proof-of-concept study showed, for example, that an established inverse relationship between distinct systems, namely muscle mass and heart rate, emerged from echocardiographic measures of heart structure and function in a genetically heterogeneous population (7). Then, with single-gene mutations as perturbations, conserved and compromised network features were identified as clues to the mechanistic and systems basis for cardiac homeostasis and dysfunction. This paradigm represents a powerful strategy to solve systems problems such as the coordination of physiological functions within and among organs that are difficult to address with conventional reductionist approaches. An important goal of systems genetics is identifying targets for modulating phenotypic outcomes to treat and prevent disease. This is difficult to achieve, however, because the sensitivity of particular trait relationships to perturbation is usually not evident. Modifier genes—variants in one gene that modulate the phenotypic manifestations of another gene—could be an efficient means of identifying such network targets. These variant genes often restore normal biological functionality despite the presence of the original disease-causing gene. Modifier effects are ubiquitous in both simple and complex traits in many organisms (8, 9). Examples in the mouse include modifiers that modulate the extent of Purkinje cell loss and dysfunction in models of neurodegenerative diseases (10), and others that control the severity of type 2 diabetes in obese mice (11). An especially exciting application of modifier genetics is the use of complete genome sequencing of families (12) that show variation in the clinical presentation of disease. With the increasing power of new technologies to provide complete genome sequences at dramatically reduced costs, systematic surveys to identify modifier genes should now be possible in humans and model organisms. Although the goal of understanding how genetic and phenotypic variants interact to create the functional diversity of organismal biology has not changed since Mendel, the experimental and computational methods of systems genetics will finally enable studies of previously intractable problems. For example, it may be possible to determine whether genetic networks governing different biological processes (development versus physiology, for example) have distinct network features, structures, and parameters. In addition, variation in robustness, criticality, and other systems properties can be studied among individuals or populations, or in healthy versus disease states. Finally, computational models of underlying network architectures and properties can be developed to predict phenotypic outcome in response to different genetic backgrounds, environmental factors, or targeted perturbations aimed at reversing disease outcome. Systems genetics is now poised to address these and other fundamental questions in biology and medicine.

Methods for detection and quantification of analytes in complex mixtures

Abstract: The invention provides a diverse population of uniquely labeled probes, containing about thirty or more target specific nucleic acid probes each attached to a unique label bound to a nucleic acid. Also provided is a method of producing a population of uniquely labeled nucleic acid probes. The method consists of (a) synthesizing a population of target specific nucleic acid probes each having a different specifier; (b) synthesizing a corresponding population of anti-genedigits each having a unique label, the population having a diversity sufficient to uniquely hybridize to genedigits within the specifiers, and (c) hybridizing the populations of target nucleic acid probes to the anti-genedigits, to produce a population in which each of the target specific probes is uniquely labeled. Also provided is a method of detecting a nucleic acid analyte. The method consists of (a) contacting a mixture of nucleic acid analytes under conditions sufficient for hybridization with a plurality of target specific nucleic acid probes each having a different specifier; (b) contacting the mixture under conditions sufficient for hybridization with a corresponding plurality of anti-genedigits each having a unique label, the plurality of anti-genedigits having a diversity sufficient to uniquely hybridize to genedigits within the specifiers, and (c) uniquely detecting a hybridized complex between one or more analytes in the mixture, a target specific probe, and an anti-genedigit.

The mobile nucleoporin Nup2p and chromatin-bound Prp20p function in endogenous NPC-mediated transcriptional control

Abstract: Nuclear pore complexes (NPCs) govern macromolecular transport between the nucleus and cytoplasm and serve as key positional markers within the nucleus. Several protein components of yeast NPCs have been implicated in the epigenetic control of gene expression. Among these, Nup2p is unique as it transiently associates with NPCs and, when artificially tethered to DNA, can prevent the spread of transcriptional activation or repression between flanking genes, a function termed boundary activity. To understand this function of Nup2p, we investigated the interactions of Nup2p with other proteins and with DNA using immunopurifications coupled with mass spectrometry and microarray analyses. These data combined with functional assays of boundary activity and epigenetic variegation suggest that Nup2p and the Ran guanylyl-nucleotide exchange factor, Prp20p, interact at specific chromatin regions and enable the NPC to play an active role in chromatin organization by facilitating the transition of chromatin between activity states.